Dependence of Self-Assembled Peptide Hydrogel Network Structure on Local Fibril Nanostructure

Physically cross-linked, fibrillar hydrogel networks are formed by the self-assembly of β-hairpin peptide molecules with varying degrees of strand asymmetry. The peptide registry in the self-assembled state can be used as a design element to generate fibrils with twisting, nontwisting, or laminated morphology. The mass density of the networks varies significantly, and can be directly related to the local fibrillar morphology as evidenced by small angle neutron scattering (SANS) and in situ substantiation using cryogenic transmission electron microscopy (cryo-TEM) under identical concentrations and conditions. Similarly, the density of the network is dependent on changes in the peptide concentration. Bulk rheological properties of the hydrogels can be correlated to the fibrillar nanostructure, with the stiffer, laminated fibrils forming networks with a higher G′ as compared to the flexible, singular fibrillar networks

EDSC: Efficient document subspace clustering technique for high-dimensional data

With the advancement in the pervasive technology, there is a spontaneous rise in the size of the data. Such data are generated from various forms of resources right from individual to organization level. Due to the characteristics of unstructured or semi-structuredness in data representation, the existing data analytics approaches are not directly applicable which leads to curse of dimensionality problem. Hence, this paper presents an Efficient Document Subspace Clustering (EDSC) technique for high-dimensional data that contributes to the existing system with respect to identification by eliminating the redundant data. The discrete segmentation of data points are used to explicitly expose the dimensionality of hidden subspaces in the clusters. The outcome of the proposed system was compared with existing system to find the effective document clustering process for high-dimensional data. The processing time of EDSC for subspace clustering is reduced by 50% as compared to the existing system

Nitrate pollution in groundwater: its causes and effects in central part of Suvarnamukhi River Basin, Karnataka

Groundwater is the major source of drinking water in the sub-​basins of the central part of Suvarnamukhi River Basin. Chem. anal. is carried out for 55 groundwater samples collected during pre-​monsoon and post-​monsoon seasons from five sub-​basins. The av. nitrate concn. is 65.96 and 97.17 ppm in pre-​monsoon and post-​monsoon seasons resp. According to Bureau of Indian Stds. (BIS)​, the max. desirable and permissible limit of nitrate is 45 ppm. In the study area, 25 samples (45​%) and 32 samples (58​%) in pre-​monsoon and post-​monsoon seasons are not suitable for drinking purpose with a seasonal variation of 43​%. Seven samples of post monsoon have increased NO3-​ concn. compared to pre-​monsoon season. The interrelationship of nitrate with other cations and anions suggest the most possible sources of nitrate as non-​point sources (leaching mechanism of nitrate due to extensive use of fertilizers) and to some extent point sources (cattle sheds and poultry farms, leakages from septic tanks, sewerage effluents)​. The nitrate distribution map shows anomalous zones in the central and south eastern part of the study area in both pre and post-​monsoon seasons suggesting that groundwater here is completely polluted and is unfit for drinking. The north western portion of the study area has high NO3-​ concn. during post monsoon season suggesting that the water is polluted due to application of nitrate rich fertilizers

Distributed Colony-Level Algorithm Switching for Robot Swarm Foraging

Swarm robotics utilizes a large number of simple robots to accomplish a task, instead of a single complex robot. Communications constraints often force these systems to be distributed and leaderless, placing restrictions on the types of algorithms which can be executed by the swarm. The performance of a swarm algorithm is affected by the environment in which the swarm operates. Different environments may call for different algorithms to be chosen, but often no single robot has enough information to make this decision. In this paper, we focus on foraging as a multi-robot task and present two distributed foraging algorithms, each of which performs best for different food locations. We then present a third adaptive algorithm in which the swarm as a whole is able to choose the best algorithm for the given situation by combining individual-level and distributed colony-level algorithm switching. We show that this adaptive method combines the bene ts of the other methods, and yields the best overall performance.Engineering and Applied Science

Two Foraging Algorithms for Robot Swarms Using Only Local Communication

Large collections of robots have the potential to perform tasks collectively using distributed control algorithms. These algorithms require communication between robots to allow the robots to coordinate their behavior and act as a collective. In this paper we describe two algorithms which allow coordination between robots, but do not require physical environment marks such as pheromones. Instead, these algorithms rely on simple, local, low bandwidth, direct communication between robots. We describe the algorithms and measure their performance in worlds with and without obstacles.Engineering and Applied Science

Efficacy and safety of secukinumab administration by autoinjector in patients with psoriatic arthritis: results from a randomized, placebo-controlled trial (FUTURE 3)

Background: The study aimed to assess 52-week efficacy and safety of secukinumab self-administration by autoinjector in patients with active psoriatic arthritis (PsA) in the FUTURE 3 study (ClinicalTrials.gov NCT01989468). Methods: Patients (≥ 18 years of age; N = 414) with active PsA were randomized 1:1:1 to subcutaneous (s.c.) secukinumab 300 mg, 150 mg, or placebo at baseline, weeks 1, 2, 3, and 4, and every 4 weeks thereafter. Per clinical response, placebo-treated patients were re-randomized to s.c. secukinumab 300 or 150 mg at week 16 (nonresponders) or week 24 (responders) and stratified at randomization by prior anti-tumor necrosis factor (TNF) therapy (anti-TNF-naïve, 68.1%; intolerant/inadequate response (anti-TNF-IR), 31.9%). The primary endpoint was the proportion of patients achieving at least 20% improvement in American College of Rheumatology response criteria (ACR20) at week 24. Autoinjector usability was evaluated by Self-Injection Assessment Questionnaire (SIAQ). Results: Overall, 92.1% (300 mg), 91.3% (150 mg), and 93.4% (placebo) of patients completed 24 weeks, and 84.9% (300 mg) and 79.7% (150 mg) completed 52 weeks. In the overall population (combined anti-TNF-naïve and anti-TNF-IR), ACR20 response rate at week 24 was significantly higher in secukinumab groups (300 mg, 48.2% (p < 0.0001); 150 mg, 42% (p < 0.0001); placebo, 16.1%) and was sustained through 52 weeks. SIAQ results showed that more than 93% of patients were satisfied/very satisfied with autoinjector usage. Secukinumab was well tolerated with no new or unexpected safety signals reported. Conclusions: Secukinumab provided sustained improvements in signs and symptoms in active PsA patients through 52 weeks. High acceptability of autoinjector was observed. The safety profile was consistent with that reported previously

RMSC: Robust Modeling of Subspace Clustering for high dimensional data

Subspace clustering is one of the active research problem associated with high-dimensional data. Here some of the standard techniques are reviewed to investigate existing methodologies. Although, there have been various forms of research techniques evolved recently, they do not completely mitigate the problems pertaining to noise sustainability and optimization of clustering accuracy. Hence, a novel technique called as Robust Modeling of Subspace Clustering (RMSC) presented to solve the above problem. An analytical research methodology is used to formulate two algorithms for computing outliers and for extracting elite subspace from the highdimensional data inflicted by different forms of noise. RMSC was found to offer higher accuracy and lower error rate both in presence of noise and absence of noise over high-dimensional data. © 2017 IEEE