IN SILICO IDENTIFICATION AND CHARACTERIZATION OF MUTUAL EPITOPE-BASED SUBUNIT VACCINE AGAINST EBOLA VIRUS AND MARBURG VIRUS

Objective: The objective of the study was to analyze the mutual epitope-based vaccine that can evoke immune response against hemorrhagic fever caused by Ebola virus (EBOV) and Marburg virus (MARV). Methodology: VP40, VP24, VP35, VP30, and NP proteins of Ebola and MARV were recovered from the protein database and subjected to many bioinformatics tools to predict the best B- and T-cell epitopes. And finally, the selected proteins were subjected to molecular docking human leukocyte antigen (HLA)-DR (major histocompatibility complex [MHC] Class I and II) to confirm their antigenicity in silico. Results: The epitopes from EBOV were stable while were unstable from MARV. Further, molecular docking simulation using most significant MHC Class II and Class I molecules demonstrated that their epitopes may bind within HLA-binding affinity to evoke an immune response. Conclusions: In this study, the data revealed the epitopes from VP40 protein could be the specific target for peptide-based vaccine design against Ebola and MARV

Secure device identification using multidimensional mapping

In this paper we investigate several potential hardware features from multiple devices for suitability during the employment of a device identification. The generation of stable and unique digital identity from features is challenging in device identification because of the unstable operation environments that implies the features employed are likely to vary under normal operating conditions. To address this, we introduce a novel multi-dimensional key generation technology which maps from multi-dimensional feature space directly to a key space. Furthermore, normalized distributions of features give the necessary data to model the characteristics, from which we derive intra-sample device feature distributions, and correlate the distinct features to generate a secure key to identify the device. Furthermore, to evaluate our experiment, we considerably carried out measurement using the mathematical & statistical modelling

Determination of EC50 of Cd and evaluation of growth and biochemical response of Palak plants (Beta vulgaris L.) to different Cd treatments.

This study was designed to evaluate the growth, biochemical response and Cd accumulation pattern of Palak (Beta vulgaris), variety of All green H1 plants for a range of Cd treatments [control (0), 10 mg/L, 20 mg/L, 30 mg/L, 40 mg/L, 50 mg/L) at two sampling stages, 25 days after germination (25 DAG) and 50 DAG. The present research also quantified the EC50 value of Cd for 50% biomass inhibition in palak plants. Increasing Cd concentration had pessimistic effects on growth and biomass. Plant height, biomass, total leaf area and yield decreased significantly on increasing Cd treatment at the two sampling stages (p<0.05). EC50 for biomass reduction was found to be 27.42 mg/L. Total phenols, thiols and MDA content elevated on increasing Cd concentration. Photosynthetic pigments, chlorophyll a and b reduced significantly by 50.81% and 48.88%, respectively at highest 50 mg/L Cd treatment. A significant interaction (p<0.05) of Cd treatment × plant age was found on Cd content analysis at the two sampling stages, Cd content increased with increasing treatment duration

Determination of EC50 of Cd and Evaluation of Growth and Biochemical Response of Palak Plants (Beta Vulgaris) to Different Cd Treatments

491-498This study was designed to evaluate the growth, biochemical response and Cd accumulation pattern of Palak (Beta vulgaris), variety of All green H1 plants for a range of Cd treatments [control (0), 10 mg/L, 20 mg/L, 30 mg/L, 40 mg/L, 50 mg/L) at two sampling stages, 25 days after germination (25 DAG) and 50 DAG. The present research also quantified the EC50 value of Cd for 50% biomass inhibition in palak plants. Increasing Cd concentration had pessimistic effects on growth and biomass. Plant height, biomass, total leaf area and yield decreased significantly on increasing Cd treatment at the two sampling stages (p<0.05). EC50 for biomass reduction was found to be 27.42 mg/L. Total phenols, thiols and MDA content elevated on increasing Cd concentration. Photosynthetic pigments, chlorophyll a and b reduced significantly by 50.81% and 48.88%, respectively at highest 50 mg/L Cd treatment. A significant interaction (p<0.05) of Cd treatment × plant age was found on Cd content analysis at the two sampling stages, Cd content increased with increasing treatment duration

New late-time constraints on f(R)f(R) gravity

Modification of general relativity (GR) inspired by theories like $f(R)$ gravity is among the most popular ones to explain the late-time acceleration of the Universe as an alternative to the $\Lambda$CDM model. In this work, we use the state-of-the-art BAO+BBN data and the most recent Type Ia supernovae (SNe Ia) sample namely PantheonPlus, including the Cepheid host distances and covariance from SH0ES samples, to robustly constrain the $f(R)$ gravity framework via two of the most popular $f(R)$ models in literature, namely, the Hu-Sawicki and Starobinsky models. Additionally, we consider how the time variation of the Newton's gravitational constant affects the supernovae distance modulus relation. We find a minor evidence for $f(R)$ gravity under the Hu-Sawicki dynamics from BAO+BBN and BAO+BBN+uncalibrated supernovae joint analysis, but the inclusion of Cepheid host distances, makes the model compatible with GR. Further, we notice tendency of this model to relax the $H_0$ tension. In general, in all the analyses carried out in this study with the late time probes, we find both the $f(R)$ models to be consistent with GR at 95\% CL.Comment: 9 pages, 3 figures, 1 table, matches the version published in Physics of Dark Univers

Device Identification Using Discrete Wavelet Transform

This paper investigates the effectiveness of employing measured hardware features mapped into the frequency domain for devices identification. The technique is to utilize Discrete Wavelet Transform (DWT) coefficients as distinguishing features. The DWT coefficients address the degree of relationship between the investigated features and the wavelet function at different occurrences of time. Therefore, DWT coefficients carry useful temporal information about the transient activity of the investigated wavelet features. We study the impacts of utilizing different wavelet functions (Coiflets, Haar and Symlets) on the performance of the device identification system. This system yields 92.5 % of accuracy using Sym6 wavelet. A comparison is made of the accuracy of wavelet features and raw features with standard classifiers

SortAlgo-Metrics: Identification of Cloud-Based Server Via a Simple Algorithmic Analysis

This paper introduces a novel technique to detect spoof or fake software systems via the generation of a unique digital signature based on a direct analysis of the construction of the system. Specifically, we model a novel mechanism referred to as SortAlgo-Metrics analysis to identify cloud-based servers. Experimentally, we deployed four cloud-based servers to run four sorting algorithms in order to extract features that are employed to perform statistical analysis upon with the aim to obtain their metrics which has further underpin the investigation of their behaviours. The model has been validated by comparing training data and unknown data, and the result has shown server 2-4 have a strong identification with 96% probability, while server 1 with 55%, it is surmised that is could be as the result of insufficient sample data. However, if such a simple model can produce a result with this high probability, this shows that with more complex features and sufficient data pulled from cloud-based servers, SortAlgo-Metrics model could generate a higher degree of basis numbers for ICMetrics technology entropy key generation and other complex systems

Robust Device Authentication Using Non-Standard Classification Features

This paper investigates the use of novel hardware features derived from the physical and behavioral characteristics of electronic devices to identify such devices uniquely. Importantly, the features examined exhibit non-standard and multimodal distributions which present a significant challenge to model and characterize. Specifically, the potency of four data classification methods is compared whilst employing such characteristics, proposed model Multivariate Gaussian Distribution (MVGD -address multimodality), Logistic Regression (LogR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM). Performance is measured based on its accuracy, precision, recall and f measure. The experimental results reveal that by addressing multimodal features with proposed model Multivariate Gaussian Distribution classifier, the overall performance is better than the other classifiers

Typification and taxonomic notes on species of Muhlenbergia Schreb. (Poaceae, Muhlenbergiinae) in India

The present paper deals with typification for two names in the genus Muhlenbergia i.e. M. duthieana and M. himalayensis. Furthermore, taxonomy of all Indian species for the same has been provided along with taxonomic key for correct identification

An E-learning component for agriculture graduates/post graduates

The agricultural education system has undergone several changes in India from past few decades. There are different courses at graduate and postgraduate level for imparting training to the agriculture students to improve agriculture and allied sectors in our country. An e-learning course has been developed, to make the freshly passed agriculture students and agri business field assistants suitable for agri business industries. This study was conducted in Ikisan Limited, Hyderabad, Andhra Pradesh during 2009 and 2010. To develop the course, surveys were conducted with agriculture students and individuals at managerial level in agri business industries. The gaps that exist between knowledge/skills/attitudes for agriculture students and agri business industries were analyzed. The courses were selected based on gap analysis to assist the students in structuring their career. The content for the courses were obtained from various sources, made into IMS content package using eXe editor and were uploaded in Moodle which is a Learning Management System. The users can access the course online according to their convenience from any place. This module is useful and provides flexibility to working professionals and students who may not attend class room teaching