Two time constants for the binding of proteins to DNA from micromechanical data

Recent experimental advances allow the direct measurement of the force/extension behavior for DNA in the presence of strongly binding proteins. Such experiments reveal information about the cooperative mechanism of protein binding. We have studied the irreversible binding of such proteins to DNA using a simple simulation and present a method for estimating quantitative rate constants for the nucleation and growth of linear domains of proteins bound to DNA. Such rate constants also give information about the relative energetics of the two binding processes. We discuss our results in the context of recent data for the DNA-recA-ATPγs system, for which the nucleation time is 4.7 × 104 min per recA binding site and the total growth rate of each domain is 1400 recA/min

Bio-signals compression using auto-encoder

Latest developments in wearable devices permits un-damageable and cheapest way for gathering of medical data such as bio-signals like ECG, Respiration, Blood pressure etc. Gathering and analysis of various biomarkers are considered to provide anticipatory healthcare through customized applications for medical purpose. Wearable devices will rely on size, resources and battery capacity; we need a novel algorithm to robustly control memory and the energy of the device. The rapid growth of the technology has led to numerous auto encoders that guarantee the results by extracting feature selection from time and frequency domain in an efficient way. The main aim is to train the hidden layer to reconstruct the data similar to that of input. In the previous works, to accomplish the compression all features were needed but in our proposed framework bio-signals compression using auto-encoder (BCAE) will perform task by taking only important features and compress it. By doing this it can reduce power consumption at the source end and hence increases battery life. The performance of the result comparison is done for the 3 parameters compression ratio, reconstruction error and power consumption. Our proposed work outperforms with respect to the SURF method

Self-stabilizing network orientation algorithms in arbitrary rooted networks

Network orientation is the problem of assigning different labels to the edges at each processor, in a globally consistent manner. A self-stabilizing protocol guarantees that the system will arrive at a legitimate state in finite time, irrespective of the initial state of the system. Two deterministic distributed network orientation protocols on arbitrary rooted, asynchronous networks are proposed in this work. Both protocols set up a chordal sense of direction in the network. The protocols are self-stabilizing, meaning that starting from an arbitrary state, the protocols are guaranteed to reach a state in which every processor has a valid node label and every link has a valid edge label. The first protocol assumes an underlying depth-first token circulation protocol; it orients the network as the token is passed among the nodes and stabilizes in O(n) steps after the token circulation stabilizes, where n is the number of processors in the network. The second protocol is designed on an underlying spanning tree protocol and stabilizes in O(h) time, after the spanning tree is constructed, where h is the height of the spanning tree. Although the second protocol assumes the existence of a spanning tree of the rooted network, it orients all edges--both tree and non-tree edges--of the network

Study on RAPD Molecular Distinction in Mulberry Varieties

The present investigation revealed that the mulberry plants are dioceious and cross pollinated with each other to produce fertile hybrid revealed a closer genetic relationship. This cannot be noticed at the species level. Usually traditional methods like morphological characters/traits are not very successful in establishing the diversity and relationship among different mulberry varieties because of environment influence. PCR based molecular marker method, RAPD was employed to study the genetic diversity and inter-relationships among 14 mulberry varieties. On an average, RAPD analysis generated 43 discrete bands/varieties with 5 arbitrary primers. The size of the amplified products ranged from 300-5000 bp with an average of 2-4 bands per primer. Of 43 amplified fragments, 41 were polymorphic (94%) with at least one pair-wise comparison between 14 varieties. RAPD analysis identified varieties specific amplification products, which will be useful in germplasm classification and introgression studies. These results suggest that RAPD based markers are useful for genetic characterization of mulberry speices/ varieties