Identification of local variations within secondary structures of proteins

Secondary-structure elements (SSEs) play an important role in the folding of proteins. Identification of SSEs in proteins is a common problem in structural biology. A new method, ASSP (Assignment of Secondary Structure in Proteins), using only the path traversed by the C atoms has been developed. The algorithm is based on the premise that the protein structure can be divided into continuous or uniform stretches, which can be defined in terms of helical parameters, and depending on their values the stretches can be classified into different SSEs, namely -helices, 3(10)-helices, -helices, extended -strands and polyproline II (PPII) and other left-handed helices. The methodology was validated using an unbiased clustering of these parameters for a protein data set consisting of 1008 protein chains, which suggested that there are seven well defined clusters associated with different SSEs. Apart from -helices and extended -strands, 3(10)-helices and -helices were also found to occur in substantial numbers. ASSP was able to discriminate non--helical segments from flanking -helices, which were often identified as part of -helices by other algorithms. ASSP can also lead to the identification of novel SSEs. It is believed that ASSP could provide a better understanding of the finer nuances of protein secondary structure and could make an important contribution to the better understanding of comparatively less frequently occurring structural motifs. At the same time, it can contribute to the identification of novel SSEs. A standalone version of the program for the Linux as well as the Windows operating systems is freely downloadable and a web-server version is also available at .

Analysis of OSNR Requirements for Higher Modulation Schemes for Various Local Oscillator Powers

To meet the growing demands of data traffic in long haul communication, it is necessary to efficiently use the low-loss region(C-band) of the optical spectrum, by increasing the no. of optical channels and increasing the bit rate on each channel But narrow pulses occupy higher spectral bandwidth. To circumvent this problem, higher order modulation schemes such as QPSK and QAM can be used to modulate the bits, which increases the spectral efficiency without demanding any extra spectral bandwidth. On the receiver side, to meet a satisfy, a given BER, the received optical signal requires to have minimum OSNR. In our study in this paper, we analyses for different modulation schemes, the OSNR required with and without preamplifier. The theoretical limit of OSNR requirement for a modulation scheme is compared for a given link length by varying the local oscillator (LO) power. Our analysis shows that as we increase the local oscillator (LO) power, the OSNR requirement decreases for a given BER. Also a combination of preamplifier and local oscillator (LO) gives the OSNR closest to theoretical limit.

Robust local and nonlocal transport in the topological Kondo insulator SmB6 in the presence of a high magnetic field

SmB6 has been predicted to be a Kondo topological insulator with topologically protected conducting surface states. We have studied quantitatively the electrical transport through surface states in high-quality single crystals of SmB6. We observe a large nonlocal surface signal at temperatures lower than the bulk Kondo gap scale. Measurements and finite-element simulations allow us to distinguish unambiguously between the contributions from different transport channels. In contrast to general expectations, the electrical transport properties of the surface channels were found to be insensitive to high magnetic fields. We propose possible scenarios that might explain this unexpected finding. Local and nonlocal magnetoresistance measurements allowed us to identify possible signatures of helical spin states and strong interband scattering at the surface.

Active learning in keyword search-based data integration

The problem of scaling up data integration, such that new sources can be quickly utilized as they are discovered, remains elusive: Global schemas for integrated data are difficult to develop and expand, and schema and record matching techniques are limited by the fact that data and metadata are often under-specified and must be disambiguated by data experts. One promising approach is to avoid using a global schema, and instead to develop keyword search-based data integration-where the system lazily discovers associations enabling it to join together matches to keywords, and return ranked results. The user is expected to understand the data domain and provide feedback about answers' quality. The system generalizes such feedback to learn how to correctly integrate data. A major open challenge is that under this model, the user only sees and offers feedback on a few ``top-'' results: This result set must be carefully selected to include answers of high relevance and answers that are highly informative when feedback is given on them. Existing systems merely focus on predicting relevance, by composing the scores of various schema and record matching algorithms. In this paper, we show how to predict the uncertainty associated with a query result's score, as well as how informative feedback is on a given result. We build upon these foundations to develop an active learning approach to keyword search-based data integration, and we validate the effectiveness of our solution over real data from several very different domains.