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When a drug enters an organism, interactions between the drug and proteins in the organism play a vital role in the storage, transport and metabolism of the drug and also affect its nonspecific toxicity, targeting and pharmacodynamic activity. However, monitoring the interaction process is a great challenge in the research of the absorption, transport and metabolic processes of drugs. In this study, we used reflectometric interference spectroscopy (RIfS) and silica colloidal crystal (SCC) film as a sensing platform to detect the binding affinity between human serum albumin (HSA) and indomethacin. SCC films composed of three silica nanospheres with different diameters were fabricated using the vertical evaporation method. HSA was immobilized covalently on SCC film using a very simple approach, and optical thickness was used as a parameter to evaluate the process of drug absorption and desorption. Finally, the optimal SCC film was selected, and three drugs other than indomethacin (i.e., warfarin, salicylic acid and quinine) were used for the validation of this sensing platform. The results verified that SCC film using RIfS is a simple and real-time sensing platform for detecting the affinity between HSA and drugs, which may be widely used in drug development and clinical testing in the future.

In the processing of carbon nanotube/polymer composites, the interactions between the nanotube and polymer matrix will occur at the molecular level. Understanding their interactions before curing is crucial for nanocomposites processing. In this study, molecular dynamics (MD) simulations were employed to reveal molecular interactions between (10, 10) single-walled nanotube and two kinds of epoxy resin systems. The two kinds of resin systems were EPON 862/EPI-CURE W curing agent (DETDA) and DGEBA (diglycidylether of bisphenol A)diethylenetriamine (DETA) curing agent. The MD simulation results show that the EPON 862, DETDA and DGEBA molecules had strong attractive interactions with single-walled nanotubes and their molecules changed their conformation to align their aromatic rings parallel to the nanotube surface due to π-stacking effect, whereas the DETA molecule had a repulsive interaction with the single-walled nanotubes. The interaction energies of the molecular systems were also calculated. Furthermore, an affinity index (AI) of the average distance between the atoms of the resin molecule and nanotube surface was defined to quantify the affinities between the nanotubes and resin molecules. The MD simulation results show that the EPON 862/EPI-CURE W curing agent system has good affinities with single-walled nanotubes.

We review our approach to the second law of thermodynamics as a theorem asserting the growth of the mean (Gibbs–von Neumann) entropy of quantum spin systems undergoing automorphic (unitary) adiabatic transformations. Non-automorphic interactions with the environment, although known to produce on the average a strict reduction of the entropy of systems with finite number of degrees of freedom, are proved to conserve the mean entropy on the average. The results depend crucially on two properties of the mean entropy, proved by Robinson and Ruelle for classical systems and Lanford and Robinson for quantum lattice systems: upper semicontinuity and affinity.

There is a widespread intuitive sense that people prefer participating in spreading the information in which they are interested. The affinity of people with information disseminated can affect the information propagation in social networks. In this paper, we propose an information diffusion model incorporating the mechanism of affinity of people with information which considers the fitness of affinity values of people with affinity threshold of the information. We find that the final size of information diffusion is affected by affinity threshold of the information, average degree of the network and the probability of people's losing their interest in the information. We also explore the effects of other factors on information spreading by numerical simulations and find that the probabilities of people's questioning and confirming the information can affect the propagation speed, but not the final scope.

The identification of transcription factor binding sites commonly relies on the interpretation of scores generated by a position weight matrix. These scores are presumed to reflect on the affinity of the transcription factor for the bound sequence. In almost all applications, a cutoff score is chosen to distinguish between functional and non-functional binding sites. This cutoff is generally based on statistical rather than biological criteria. Furthermore, given the variety of transcription factors, it is unlikely that the use of a common statistical threshold for all transcription factors is appropriate. In order to incorporate biological information into the choice of cutoff score, we developed a simple evolutionary model that assumes that transcription factor binding sites evolve to maintain an affinity greater than some factor-specific threshold. We then compared patterns of substitution in binding sites predicted by this model at different thresholds to patterns of substitution observed at sites bound in vivo by transcription factors in S. cerevisiae. Assuming that, the cutoff value that, gives the best fit between the observed and predicted values will optimally distinguish functional and non-functional sites, we discovered substantial heterogeneity for appropriate cutoff values among factors. While commonly used thresholds seem appropriate for many factors, some factors appear to function at cutoffs satisfied commonly in the genome. This evidence was corroborated by local patterns of rate variation for examples of stringent and lenient p-value cutoffs. Our analysis further highlights the necessity of taking a factor-specific approach to binding site identification.