structural, referential and normative information

This article provides a comprehensive conceptual analysis of information. It begins with a folk notion that information is a tripartite phenomenon: information is something carried by signals about something for some use. This suggests that information has three main aspects: structural, referential, and normative. I analyze the individually necessary and jointly sufficient conditions for defining these aspects of information and consider formal theories relating to each aspect as well. The analysis reveals that structural, referential, and normative aspects of information are hierarchically nested and that the normative depends on the referential, which in turn depends on the structural

Consensus of self-driven agents with avoidance of collisions

In recent years, many efforts have been addressed on collision avoidance of collectively moving agents. In this paper, we propose a modified version of the Vicsek model with adaptive speed, which can guarantee the absence of collisions. However, this strategy leads to an aggregated state with slowly moving agents. We therefore further introduce a certain repulsion, which results in both faster consensus and longer safe distance among agents, and thus provides a powerful mechanism for collective motions in biological and technological multi-agent systems.Comment: 8 figures, and 7 page

Human Microbe-Disease Association Prediction Based on Adaptive Boosting

There are countless microbes in the human body, and they play various roles in the physiological process. There is growing evidence that microbes are closely associated with human diseases. Researching disease-related microbes helps us understand the mechanisms of diseases and provides new strategies for diseases diagnosis and treatment. Many computational models have been proposed to predict disease-related microbes, in this paper, we developed a model of Adaptive Boosting for Human Microbe-Disease Association prediction (ABHMDA) to reveal the associations between diseases and microbes by calculating the relation probability of disease-microbe pair using a strong classifier. Our model could be applied to new diseases without any known related microbes. In order to assess the prediction power of the model, global and local leave-one-out cross validation (LOOCV) were implemented. As shown in the results, the global and local LOOCV values reached 0.8869 and 0.7910, respectively. What’s more, 10, 10, and 8 out of the top 10 microbes predicted to be most likely to be associated with Asthma, Colorectal carcinoma and Type 1 diabetes were all verified by relevant literatures or database HMDAD, respectively. The above results verify the superior predictive performance of ABHMDA

Accelerating consensus of self-driven swarm via adaptive speed

In resent years, Vicsek model has attracted more and more attention and been well developed. However, the in-depth analysis on the convergence time are scarce thus far. In this paper, we study some certain factors that mainly govern the convergence time of Vicsek model. By extensively numerical simulations, we find the convergence time scales in a power law with $r^2\ln N$ in the noise-free case, where $r$ and $N$ are horizon radius and the number of particles. Furthermore, to accelerate the convergence, we propose a new model in which the speed of each particle is variable. The convergence time can be remarkably shortened compared with the standard Vicsek model.Comment: 11 pages, 6 figure

Biphasic activation of PI3K/Akt and MAPK/Erk1/2 signaling pathways in bovine herpesvirus type 1 infection of MDBK cells

Many viruses have been known to control key cellular signaling pathways to facilitate the virus infection. The possible involvement of signaling pathways in bovine herpesvirus type 1 (BoHV-1) infection is unknown. This study indicated that infection of MDBK cells with BoHV-1 induced an early-stage transient and a late-stage sustained activation of both phosphatidylinositol 3-kinase (PI3K)/Akt and mitogen activated protein kinases/extracellular signal-regulated kinase 1/2 (MAPK/Erk1/2) signaling pathways. Analysis with the stimulation of UV-irradiated virus indicated that the virus binding and/or entry process was enough to trigger the early phase activations, while the late phase activations were viral protein expression dependent. Biphasic activation of both pathways was suppressed by the selective inhibitor, Ly294002 for PI3K and U0126 for MAPK kinase (MEK1/2), respectively. Furthermore, treatment of MDBK cells with Ly294002 caused a 1.5-log reduction in virus titer, while U0126 had little effect on the virus production. In addition, the inhibition effect of Ly294002 mainly occurred at the post-entry stage of the virus replication cycle. This revealed for the first time that BoHV-1 actively induced both PI3K/Akt and MAPK/Erk1/2 signaling pathways, and the activation of PI3K was important for fully efficient replication, especially for the post-entry stage

Compound dietary fiber and high-grade protein diet improves glycemic control and ameliorates diabetes and its comorbidities through remodeling the gut microbiota in mice

Dietary intervention with a low glycemic index and full nutritional support is emerging as an effective strategy for diabetes management. Here, we found that the treatment of a novel compound dietary fiber and high-grade protein diet (CFP) improved glycemic control and insulin resistance in streptozotocin-induced diabetic mice, with a similar effect to liraglutide. In addition, CFP treatment ameliorated diabetes-related metabolic syndromes, such as hyperlipidemia, hepatic lipid accumulation and adipogenesis, systemic inflammation, and diabetes-related kidney damage. These results were greatly associated with enhanced gut barrier function and altered gut microbiota composition and function, especially those bacteria, microbial functions, and metabolites related to amino acid metabolism. Importantly, no adverse effect of CFP was found in our study, and CFP exerted a wider arrange of protection against diabetes than liraglutide. Thereby, fortification with balanced dietary fiber and high-grade protein, like CFP, might be an effective strategy for the management and treatment of diabetes

Revealing Drug-Target Interactions with Computational Models and Algorithms

Background: Identifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational models have been exploited to infer DTI candidates. Methods: We introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods. Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning. Results: Although computational methods have obtained significant improvement in the process of DTI prediction, these models have their limitations. We discussed potential avenues for boosting DTI prediction accuracy as well as further directions