Terms Of Interaction Strength Research Articles

Rapid assessment of management options for promoting stock rebuilding in data-poor species under climate change.

The development of species recovery plans requires considering likely outcomes of different management interventions, but the complicating effects of climate change are rarely evaluated. We examined how qualitative network models (QNMs) can be deployed to support decision making when data, time, and funding limitations restrict use of more demanding quantitative methods. We used QNMs to evaluate management interventions intended to promote the rebuilding of a collapsed stock of blue king crab (Paralithodes platypus) (BKC) around the Pribilof Islands (eastern Bering Sea) to determine how their potential efficacy may change under climate change. Based on stakeholder input and a literature review, we constructed a QNM that described the life cycle of BKC, key ecological interactions, potential climate-change impacts, relative interaction strengths, and uncertainty in terms of interaction strengths and link presence. We performed sensitivity analyses to identify key sources of prediction uncertainty. Under a scenario of no climate change, predicted increases in BKC were reliable only when stock enhancement was implemented in a BKC hatchery-program scenario. However, when climate change was accounted for, the intervention could not counteract its adverse impacts, which had an overall negative effect on BKC. The remaining management scenarios related to changes in fishing effort on BKC predators. For those scenarios, BKC outcomes were unreliable, but climate change further decreased the probability of observing recovery. Including information on relative interaction strengths increased the likelihood of predicting positive outcomes for BKC approximately 5-50% under the management scenarios. The largest gains in prediction precision will be made by reducing uncertainty associated with ecological interactions between adult BKC and red king crab (Paralithodes camtschaticus). Qualitative network models are useful options when data are limited, but they remain underutilized in conservation.

Engineering asymmetric steady-state Einstein-Podolsky-Rosen steering in macroscopic hybrid systems

Generation of quantum correlations between separate objects is of significance both in fundamental physics and in quantum networks. One important challenge is to create the directional "spooky action-at-a-distanc" effects that Schr\"{o}dinger called "steering" between two macroscopic and massive objects. Here, we analyze a generic scheme for generating steering correlations in cascaded hybrid systems in which two distant oscillators with effective masses of opposite signs are coupled to a unidirectional light field, a setup which is known to build up quantum correlations by means of quantum back-action evasion. The unidirectional coupling of the first to the second oscillator via the light field can be engineered to enhance steering in both directions and provides an active method for controlling the asymmetry of steering. We show that the resulting scheme can efficiently generate unconditional steady-state Einstein-Podolsky-Rosen steering between the two subsystems, even in the presence of thermal noise and optical losses. As a scenario of particular technological interest in quantum networks, we use our scheme to engineer enhanced steering from an untrusted node with limited tunability (in terms of interaction strength and type with the light field) to a trusted, highly tunable node, hence offering a path to implementing one-sided device-independent quantum tasks.

Polymeric Micelles of Biodegradable Diblock Copolymers: Enhanced Encapsulation of Hydrophobic Drugs.

Polymeric micelles are potentially efficient in encapsulating and performing the controlled release of various hydrophobic drug molecules. Understanding the fundamental physicochemical properties behind drug–polymer systems in terms of interaction strength and compatibility, drug partition coefficient (preferential solubilization), micelle size, morphology, etc., encourages the formulation of polymeric nanocarriers with enhanced drug encapsulating capacity, prolonged circulation time, and stability in the human body. In this review, we systematically address some open issues which are considered to be obstacles inhibiting the commercial availability of polymer-based therapeutics, such as the enhancement of encapsulation capacity by finding better drug–polymer compatibility, the drug-release kinetics and mechanisms under chemical and mechanical conditions simulating to physiological conditions, and the role of preparation methods and solvents on the overall performance of micelles.

Identifying Influential People Based on Interaction Strength

Extraction of influential people from their respective domains has attained the attention of scholastic community during current epoch. This study introduces an innovative interaction strength metric for retrieval of the most influential users in the online social network. The interactive strength is measured by three factors, namely re-tweet strength, commencing intensity and mentioning density. In this article, we design a novel algorithm called IPRank that considers the communications from perspectives of followers and followees in order to mine and rank the most influential people based on proposed interaction strength metric. We conducted extensive experiments to evaluate the strength and rank of each user in the micro-blog network. The comparative analysis validates that IPRank discovered high ranked people in terms of interaction strength. While the prior algorithm placed some low influenced people at high rank. The proposed model uncovers influential people due to inclusion of a novel interaction strength metric that improves results significantly in contrast with prior algorithm.

Dynamic and structural constraints in signal propagation by regulatory networks

Regulatory networks are able to process complex signals and respond appropriately to the cellular context. Thus, an increasing effort by systems biology researchers is being focused on understanding which interactions are responsible for a given functional response. When translated into specific mathematical models, however, it has been repeatedly shown that this mapping between topology and function is not one-to-one, even for the simplest networks. Moreover, dynamical behavior may play an important role which is necessary to integrate in the general picture. We propose a unified theoretical/statistical approach to characterize the structure-function relationship in molecular networks when temporal features of both input signal and output response are important. The theory allows fast computation of network responses in terms of interaction strengths irrespective of molecular details, while statistical analysis identifies constraints between structural and dynamical features and network function. Investigating different feedback and feedforward loop architectures, we find that processing of temporal signals is strongly correlated to certain combinations of structural and dynamical characteristics, rather than to individual interactions. Our analysis offers new insight into the structure-function relationship in network motifs, quantifying how much the tuning of specific interactions affects network outcome, identifying key structural parameters for a given response and relating dynamics to network topology and function. This kind of analyses can be especially useful for synthetic biology approaches, where promoter libraries with a range of inputs and outputs can be engineered, and one has to choose the correct component needed to produce the desired network function.

Demonstration of non-linear detection in ELISA resulting in up to 1000-fold too high affinities of fibronogen binding to integrin αIIbβ3

To clarify the question as to why different solid-phase assays yield different results in terms of interaction strength, we used fibrinogen binding to immobilized αIIbβ3 integrin as a test system. A classical ‘three step’ enzyme-linked (ELISA), a ‘two step’ biotin enzyme-linked streptavidin and a ‘one step’ radioligand assay were compared under otherwise identical conditions. Only the last assay yielded binding constants comparable to earlier data by total internal reflection fluorescence microscopy while the other assays yielded apparent binding constants 5- to 1000-fold too high. These effects are explained by non-linearity of detection signals.