10,844 research outputs found
Ligand Path: A Software Tool for Mapping Dynamic Ligand Migration Channel Networks
AbstractProteins are essential compositions of the living organisms and involved in the processes of different life events. Basically proteins are like amazing tiny bio-machines performing the functions in a stable and predictable manner and understanding the underline mechanisms can facilitate the pharmaceutical development. However, protein functions are not carried in a static style, so experimental observations of these dynamic movements of the drugs inside the proteins are difficult, so computational methods have an important and irreplaceable role.We developed a software tool called LigandPath for mapping the ligand migration channels in a constantly moving protein and this software can function with CADD (Computer aided drug design) software to map the possible migration pathways of candidate drugs inside a protein. Traditionally, biologists use MD (Molecular Dynamics) simulation to locate the ligand migration channels, but it takes long time for them to observe the complete migration paths. In order to overcome the limitations of the trajectory-based MD simulation, we adopt a computational method inspired from robotic motion planning called DyME (Dynamic Map Ensemble) and we develop the software tool LigandPath based on DyME. The software tool has already been successfully applied to map the potential migration channels of drugs candidates of three proteins, PPAR (peroxisome proliferator-activated receptors), UROD (uroporphyrinogen decarboxylase) and Sirt1 (silent information regulator 1) complexes in three publications
Periodic Radio Variability in NRAO 530: Phase Dispersion Minimization Analysis
In this paper, a periodicity analysis of the radio light curves of the blazar
NRAO 530 at 14.5, 8.0, and 4.8 GHz is presented employing an improved Phase
Dispersion Minimization (PDM) technique. The result, which shows two persistent
periodic components of and years at all three frequencies,
is consistent with the results obtained with the Lomb-Scargle periodogram and
weighted wavelet Z-transform algorithms. The reliability of the derived
periodicities is confirmed by the Monte Carlo numerical simulations which show
a high statistical confidence. (Quasi-)Periodic fluctuations of the radio
luminosity of NRAO 530 might be associated with the oscillations of the
accretion disk triggered by hydrodynamic instabilities of the accreted flow.
\keywords{methods: statistical -- galaxies: active -- galaxies: quasar:
individual: NRAO 530}Comment: 8 pages, 5 figures, accepted by RA
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Modulating Linker Composition of Haptens Resulted in Improved Immunoassay for Histamine.
Histamine (HA) is an important food contaminant generated during food fermentation or spoilage. However, an immunoassay for direct (derivatization free) determination of HA has rarely been reported due to its small size to induce the desired antibodies by its current hapten-protein conjugates. In this work, despite violating the classical hapten design criteria which recommend introducing a linear aliphatic (phenyl free) linker into the immunizing hapten, a novel haptens, HA-245 designed and synthesized with a phenyl-contained linker, exhibited significantly enhanced immunological properties. Thus, a quality-improved monoclonal antibody (Mab) against HA was elicited by its hapten-carrier conjugates. Then, as the linear aliphatic linker contained haptens, Hapten B was used as linker-heterologous coating haptens to eliminate the recognition of linker antibodies. Indirect competitive ELISA (ic-ELISA) was developed with a 50% inhibition concentration (IC50) of 0.21 mg/L and a limit of detection (LOD) of 0.06 mg/L in buffer solution. The average recoveries of HA from spiked food samples for this ic-ELISA ranged from 84.1% and 108.5%, and the analysis results agreed well with those of referenced LC-MS/MS. This investigation not only realized derivatization-free immunoassay for HA, but also provided a valuable guidance for hapten design and development of immunoassay for small molecules
Spatiotemporal Propagation Learning for Network-Wide Flight Delay Prediction
Demystifying the delay propagation mechanisms among multiple airports is
fundamental to precise and interpretable delay prediction, which is crucial
during decision-making for all aviation industry stakeholders. The principal
challenge lies in effectively leveraging the spatiotemporal dependencies and
exogenous factors related to the delay propagation. However, previous works
only consider limited spatiotemporal patterns with few factors. To promote more
comprehensive propagation modeling for delay prediction, we propose
SpatioTemporal Propagation Network (STPN), a space-time separable graph
convolutional network, which is novel in spatiotemporal dependency capturing.
From the aspect of spatial relation modeling, we propose a multi-graph
convolution model considering both geographic proximity and airline schedule.
From the aspect of temporal dependency capturing, we propose a multi-head
self-attentional mechanism that can be learned end-to-end and explicitly reason
multiple kinds of temporal dependency of delay time series. We show that the
joint spatial and temporal learning models yield a sum of the Kronecker
product, which factors the spatiotemporal dependence into the sum of several
spatial and temporal adjacency matrices. By this means, STPN allows cross-talk
of spatial and temporal factors for modeling delay propagation. Furthermore, a
squeeze and excitation module is added to each layer of STPN to boost
meaningful spatiotemporal features. To this end, we apply STPN to multi-step
ahead arrival and departure delay prediction in large-scale airport networks.
To validate the effectiveness of our model, we experiment with two real-world
delay datasets, including U.S and China flight delays; and we show that STPN
outperforms state-of-the-art methods. In addition, counterfactuals produced by
STPN show that it learns explainable delay propagation patterns.Comment: 14 pages,8 figure
A Novel Adaptive Elite-Based Particle Swarm Optimization Applied to VAR Optimization in Electric Power Systems
Particle swarm optimization (PSO) has been successfully applied to solve many practical engineering problems. However, more efficient strategies are needed to coordinate global and local searches in the solution space when the studied problem is extremely nonlinear and highly dimensional. This work proposes a novel adaptive elite-based PSO approach. The adaptive elite strategies involve the following two tasks: (1) appending the mean search to the original approach and (2) pruning/cloning particles. The mean search, leading to stable convergence, helps the iterative process coordinate between the global and local searches. The mean of the particles and standard deviation of the distances between pairs of particles are utilized to prune distant particles. The best particle is cloned and it replaces the pruned distant particles in the elite strategy. To evaluate the performance and generality of the proposed method, four benchmark functions were tested by traditional PSO, chaotic PSO, differential evolution, and genetic algorithm. Finally, a realistic loss minimization problem in an electric power system is studied to show the robustness of the proposed method
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