Discovery of new mutually orthogonal bioorthogonal cycloaddition pairs through computational screening.

Density functional theory (DFT) calculations and experiments in tandem led to discoveries of new reactivities and selectivities involving bioorthogonal sydnone cycloadditions. Dibenzocyclooctyne derivatives (DIBAC and BARAC) were identified to be especially reactive dipolarophiles, which undergo the (3+2) cycloadditions with N-phenyl sydnone with the rate constant of up to 1.46 M-1 s-1. Most signifcantly, the sydnone-dibenzocyclooctyne and norbornene-tetrazine cycloadditions were predicted to be mutually orthogonal. This was validated experimentally and used for highly selective fluorescence labeling of two proteins simultaneously

Oracle NoSQL Database

Data is stored and retrieved from Database in tabular format which is called Relational Database and familiar from 1960’s as SQL (Structured Query Language) Database. Today with the rapidly increasing collected information, data driven applications are rising in science and business territories. From 21st century the term NoSQL is being popular and it is also called ‘Non SQL’ or ‘Non-Relational’ or ‘Not only SQL’. For large scale applications on datacenters or cloud distributed NoSQL systems are well known for their ease of use. There are many NoSQL non-relational Databases are introduced depending on its type based on the CAP theorem. NoSQL database data models are classified into different categories like Key- Value system, Document based system, column based system and Graph based system mainly. In this paper we will be introducing Oracle NoSQL database which is horizontally scaled with high availability, key valued database for cloud and web services. It has load balancing which is transparent even after dynamically adding new capacity

A shapley value-based approach to discover influential nodes in social networks

Our study concerns an important current problem, that of diffusion of information in social networks. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing and sales promotions. In this paper, we focus on the target set selection problem, which involves discovering a small subset of influential players in a given social network, to perform a certain task of information diffusion. The target set selection problem manifests in two forms: 1) top-k nodes problem and 2) λ-coverage problem. In the top-k nodes problem, we are required to find a set of k key nodes that would maximize the number of nodes being influenced in the network. The λ-coverage problem is concerned with finding a set of key nodes having minimal size that can influence a given percentage λ of the nodes in the entire network. We propose a new way of solving these problems using the concept of Shapley value which is a well known solution concept in cooperative game theory. Our approach leads to algorithms which we call the ShaPley value-based Influential Nodes (SPINs) algorithms for solving the top-k nodes problem and the λ-coverage problem. We compare the performance of the proposed SPIN algorithms with well known algorithms in the literature. Through extensive experimentation on four synthetically generated random graphs and six-real-world data sets (Celegans, Jazz, NIPS coauthorship data set, Netscience data set, High-Energy Physics data set, and Political Books data set), we show that the proposed SPIN approach is more powerful and computationally efficient