PPDTS: Predicting potential drug–target interactions based on network similarity

Identification of drug–target interactions (DTIs) has great practical importance in the drug discovery process for known diseases. However, only a small proportion of DTIs in these databases has been verified experimentally, and the computational methods for predicting the interactions remain challenging. As a result, some effective computational models have become increasingly popular for predicting DTIs. In this work, the authors predict potential DTIs from the local structure of drug–target associations'' network, which is different from the traditional global network similarity methods based on structure and ligand. A novel method called PPDTS is proposed to predict DTIs. First, according to the DTIs’ network local structure, the known DTIs are converted into a binary network. Second, the Resource Allocation algorithm is used to obtain a drug–drug similarity network and a target–target similarity network. Third, a Collaborative Filtering algorithm is used with the known drug–target topology information to obtain similarity scores. Fourth, the linear combination of drug–target similarity model and the target–drug similarity model are innovatively proposed to obtain the final prediction results. Finally, the experimental performance of PPDTS has proved to be higher than that of the previously mentioned four popular network‐based similarity methods, which is validated in different experimental datasets. Some of the predicted results can be supported in UniProt and DrugBank databases.     

Network morphospace

The structure of complex networks has attracted much attention in recent years. It has been noted that many real-world examples of networked systems share a set of common architectural features. This raises important questions about their origin, for example whether such network attributes reflect common design principles or constraints imposed by selectional forces that have shaped the evolution of network topology. Is it possible to place the many patterns and forms of complex networks into a common space that reveals their relations, and what are the main rules and driving forces that determine which positions in such a space are occupied by systems that have actually evolved? We suggest that these questions can be addressed by combining concepts from two currently relatively unconnected fields. One is theoretical morphology, which has conceptualized the relations between morphological traits defined by mathematical models of biological form. The second is network science, which provides numerous quantitative tools to measure and classify different patterns of local and global network architecture across disparate types of systems. Here, we explore a new theoretical concept that lies at the intersection between both fields, the ‘network morphospace’. Defined by axes that represent specific network traits, each point within such a space represents a location occupied by networks that share a set of common ‘morphological’ characteristics related to aspects of their connectivity. Mapping a network morphospace reveals the extent to which the space is filled by existing networks, thus allowing a distinction between actual and impossible designs and highlighting the generative potential of rules and constraints that pervade the evolution of complex systems.     

Analytical reasoning task reveals limits of social learning in networks

Social learning—by observing and copying others—is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an ‘unreflective copying bias’, which limits their social learning to the output, rather than the process, of their peers’ reasoning—even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.     

基于产业链演化的物流网络资源配置策略

从基础设施、信息系统、服务功能等角度分析物流网络资源,总结地理环境和商业市场因素对物流基础设施的空间构型的影响,提出从市场角度研究物流资源配置的必要性.分析社会经济系统中经济运行链式结构由企业集群至产业链的发展趋势,从服务范围、服务核心、需求市场、利用效率等层面研究不同经济运行结构条件对物流网络资源影响,从设施、信息和功能等分项角度总结瓶颈问题,提出资源配置对策.     

Collective thermoregulation in bee clusters

Swarming is an essential part of honeybee behaviour, wherein thousands of bees cling onto each other to form a dense cluster that may be exposed to the environment for several days. This cluster has the ability to maintain its core temperature actively without a central controller. We suggest that the swarm cluster is akin to an active porous structure whose functional requirement is to adjust to outside conditions by varying its porosity to control its core temperature. Using a continuum model that takes the form of a set of advection–diffusion equations for heat transfer in a mobile porous medium, we show that the equalization of an effective ‘behavioural pressure’, which propagates information about the ambient temperature through variations in density, leads to effective thermoregulation. Our model extends and generalizes previous models by focusing the question of mechanism on the form and role of the behavioural pressure, and allows us to explain the vertical asymmetry of the cluster (as a consequence of buoyancy-driven flows), the ability of the cluster to overpack at low ambient temperatures without breaking up at high ambient temperatures, and the relative insensitivity to large variations in the ambient temperature. Our theory also makes testable hypotheses for the response of the cluster to external temperature inhomogeneities and suggests strategies for biomimetic thermoregulation.     

Phase transitions and assortativity in models of gene regulatory networks evolved under different selection processes

We study a simplified model of gene regulatory network evolution in which links (regulatory interactions) are added via various selection rules that are based on the structural and dynamical features of the network nodes (genes). Similar to well-studied models of ‘explosive’ percolation, in our approach, links are selectively added so as to delay the transition to large-scale damage propagation, i.e. to make the network robust to small perturbations of gene states. We find that when selection depends only on structure, evolved networks are resistant to widespread damage propagation, even without knowledge of individual gene propensities for becoming ‘damaged’. We also observe that networks evolved to avoid damage propagation tend towards disassortativity (i.e. directed links preferentially connect high degree ‘source’ genes to low degree ‘target’ genes and vice versa). We compare our simulations to reconstructed gene regulatory networks for several different species, with genes and links added over evolutionary time, and we find a similar bias towards disassortativity in the reconstructed networks.     

A novel Monte Carlo simulation method for Ising systems

We propose a Monte Carlo method to obtain the thermodynamic functions of Ising systems. We perform a random sampling of spin configurations to determine the degeneracy of the energies of the system, from which an approximant to the partition function is determined. The main advantage of the method over conventional Metropolis lies in the fact that only a single Monte Carlo run is needed to obtain results valid for all temperatures, magnetic fields, and coupling parameters (FM or AFM). As an illustration of the method, we present results for the Ising model in a magnetic field on a 8x8 lattice. The method can be adapted to tackle the random field Ising model (RFIM), the dilute Ising model, and the Ising spin glass, in any spatial dimension.     

Social network architecture and the maintenance of deleterious cultural traits

How have changes in communications technology affected the way that misinformation spreads through a population and persists? To what extent do differences in the architecture of social networks affect the spread of misinformation, relative to the rates and rules by which individuals transmit or eliminate different pieces of information (cultural traits)? Here, we use analytical models and individual-based simulations to study how a ‘cultural load’ of misinformation can be maintained in a population under a balance between social transmission and selective elimination of cultural traits with low intrinsic value. While considerable research has explored how network architecture affects percolation processes, we find that the relative rates at which individuals transmit or eliminate traits can have much more profound impacts on the cultural load than differences in network architecture. In particular, the cultural load is insensitive to correlations between an individual''s network degree and rate of elimination when these quantities vary among individuals. Taken together, these results suggest that changes in communications technology may have influenced cultural evolution more strongly through changes in the amount of information flow, rather than the details of who is connected to whom.     

The Effect of Social Presence on Affective and Cognitive Learning in an International Engineering Course Taught via Distance Learning

Background Distance learning course formats can alter modes of information exchange and interpersonal interaction relative to traditional course formats. Purpose (Hypothesis ) To determine the effect of a distance course format on the knowledge acquisition (cognitive learning) and satisfaction (affective learning) of students, we investigated student learning responses and social presence during a graduate‐level engineering course taught via traditional (i.e., professor present in the classroom) and synchronous distance‐learning formats. Design /Method Direct quantification of participation, academic performance assessment based on homework and exam scores, and survey‐based assessments of student perceptions of the course were collected. Based on these data, cognitive and affective learning responses to different technological and interaction‐based aspects of the course were determined for each course format. Results We show that while affective learning decreased for students in the distance format course relative to the traditional format, cognitive learning was comparable. Our results suggest that loss of satellite connection and audio losses had a stronger negative effect on student perceptions than video disturbances, and that participation was the most important factor influencing affective learning. Conclusions While our findings do not suggest that cognitive learning is strongly affected by social presence, implementing strategies to enhance social presence may improve the overall learning experience and make distance learning more enjoyable for students.     

Impact of the implementation of rest days in live bird markets on the dynamics of H5N1 highly pathogenic avian influenza

Live bird markets (LBMs) act as a network ‘hub’ and potential reservoir of infection for domestic poultry. They may therefore be responsible for sustaining H5N1 highly pathogenic avian influenza (HPAI) virus circulation within the poultry sector, and thus a suitable target for implementing control strategies. We developed a stochastic transmission model to understand how market functioning impacts on the transmission dynamics. We then investigated the potential for rest days—periods during which markets are emptied and disinfected—to modulate the dynamics of H5N1 HPAI within the poultry sector using a stochastic meta-population model. Our results suggest that under plausible parameter scenarios, HPAI H5N1 could be sustained silently within LBMs with the time spent by poultry in markets and the frequency of introduction of new susceptible birds'' dominant factors determining sustained silent spread. Compared with interventions applied in farms (i.e. stamping out, vaccination), our model shows that frequent rest days are an effective means to reduce HPAI transmission. Furthermore, our model predicts that full market closure would be only slightly more effective than rest days to reduce transmission. Strategies applied within markets could thus help to control transmission of the disease.     

Single molecule dynamics in a virtual cell: a three-dimensional model that produces simulated fluorescence video-imaging data

The analysis of single molecule imaging experiments is complicated by the stochastic nature of single molecule events, by instrument noise and by the limited information which can be gathered about any individual molecule observed. Consequently, it is important to cross check experimental results using a model simulating single molecule dynamics (e.g. movements and binding events) in a virtual cell-like environment. The output of such a model should match the real data format allowing researchers to compare simulated results with the real experiments. The proposed model exploits the advantages of ‘object-oriented’ computing. First of all, the ability to create and manipulate a number of classes, each containing an arbitrary number of single molecule objects. These classes may include objects moving within the ‘cytoplasm’; objects moving at the ‘plasma membrane’; and static objects located inside the ‘body’. The objects of a given class can interact with each other and/or with the objects of other classes according to their physical and chemical properties. Each model run generates a sequence of images, each containing summed images of all fluorescent objects emitting light under given illumination conditions with realistic levels of noise and emission fluctuations. The model accurately reproduces reported single molecule experiments and predicts the outcome of future experiments.     

Cultural perspectives on contraceptive technology

The cultural setting in which people live influences how they view contraceptives and, ultimately, what method—if any—they decide to use. While a method's effectiveness, safety and convenience always are important to users, these attributes and others may be viewed differently by different groups. If specific attributes are viewed negatively, method acceptability and use will be low. It is imperative that organizations conducting contraceptive research and development take cultural factors into account so that they can design methods that will have broad appeal. Family planning programs and other groups working to introduce and adapt contraceptive methods to specific populations also need to take cultural factors into account in order to develop the most appropriate distribution channels, publicity campaigns, and educational materials. Agencies and programs working to make methods more culturally appropriate must obtain ethnographic data on how people perceive family planning, and integrate this information into their plans.     

Extracting μr and εr of solids fromone-port phasor network analyzer measurements

A method for simultaneously and rapidly extracting permittivity, ε, and permeability, μ, of nonmetallic solids over a wide range of frequencies, based on one-port reflection measurements made by an automatic network analyzer (ANA), is described. Reflection measurements are taken of a short, an offset short-circuited air line of length l , and two short-circuited samples of lengths land nl , where n is any integer >1. The information contained in these four measurements is sufficient to extract ε and μ without recourse to any a priori knowledge of the waveguide's cross section, dispersion, or loss. This method is particularly convenient for measuring ε and μ at temperatures significantly different from ambient and in waveguide cross sections whose dispersion and loss are not well known. The presence of higher order modes and of sample inhomogeneities can be detected from an additional measurement of an offset short-circuited air line of length nl. Measurements at Ka-band on polystyrene agree with those of other investigators. A number of practical considerations for successfully implementing the method discussed above, relating to sample length, time-domain gating, mode suppression, and sample fabrication, are discussed     

Establishing interoperability among knowledge organization systems for research management: a social network approach

Knowledge organization systems (KOS) have long been established as a tool to represent organized interpretation of knowledge structures. Various types of KOS such as discipline tree for research projects, subject categories for research publications, and classifications schemes for research patents have been constructed and widely used in R&D contexts. However, the incompatible KOS, together with information proliferation in the Big Data Era, impose great challenges for effective research management. How to facilitate interoperability among heterogeneous research information sources is an important problem to be solved. KOS mapping methods were proposed to provide alternative subject access by establishing equivalence or partial equivalence relationships between classes in different KOS but suffered from “lagging mapping” and “deficient mapping”. This research proposes a social network approach that leverages online social platform information (i.e., research activities and social activities) for KOS mapping. The underlying assumption behind the approach is that “two classes/terms in different KOS are related if their corresponding research objects are connected to similar researchers”. The social network approach leverages social network analysis instead of semantic and structure analysis of metadata information for mapping degree calculation. The approach has been implemented on the largest research social platform in China and successfully realizes mapping between KOS for research management purpose. We conducted mapping between National Natural Science Foundation of China discipline tree and Web of Science subject categories in this study to examine the performance of social network approach.     

Algorithm to identify the optimal perturbation based on the net basin‐of‐state of perturbed states in Boolean network

Boolean networks are widely used to model gene regulatory networks and to design therapeutic intervention strategies to affect the long‐term behavior of systems. Here, the authors investigate the 1 bit perturbation, which falls under the category of structural intervention. The authors’ idea is that, if and only if a perturbed state evolves from a desirable attractor to an undesirable attractor or from an undesirable attractor to a desirable attractor, then the size of basin of attractor of a desirable attractor may decrease or increase. In this case, if the authors obtain the net BOS of the perturbed states, they can quickly obtain the optimal 1 bit perturbation by finding the maximum value of perturbation gain. Results from both synthetic and real biological networks show that the proposed algorithm is not only simpler and but also performs better than the previous basin‐of‐states (BOS)‐based algorithm by Hu et al..Inspec keywords: perturbation theory, genetics, Boolean functionsOther keywords: optimal perturbation, perturbed states, Boolean network, gene regulatory networks, basin‐of‐states‐based algorithm, state‐transition diagram, structural intervention, perturbation gain, synthetic biological networks, real biological networks, 1 bit perturbation     

Semi-GSGCN: Social Robot Detection Research with Graph Neural Network

Malicious social robots are the disseminators of malicious information on social networks, which seriously affect information security and network environments. Efficient and reliable classification of social robots is crucial for detecting information manipulation in social networks. Supervised classification based on manual feature extraction has been widely used in social robot detection. However, these methods not only involve the privacy of users but also ignore hidden feature information, especially the graph feature, and the label utilization rate of semi-supervised algorithms is low. Aiming at the problems of shallow feature extraction and low label utilization rate in existing social network robot detection methods, in this paper a robot detection scheme based on weighted network topology is proposed, which introduces an improved network representation learning algorithm to extract the local structure features of the network, and combined with the graph convolution network (GCN) algorithm based on the graph filter, to obtain the global structure features of the network. An end-to-end semi-supervised combination model (Semi-GSGCN) is established to detect malicious social robots. Experiments on a social network dataset (cresci-rtbust-2019) show that the proposed method has high versatility and effectiveness in detecting social robots. In addition, this method has a stronger insight into robots in social networks than other methods.     

The evolution of continuous learning of the structure of the environment

Continuous, ‘always on’, learning of structure from a stream of data is studied mainly in the fields of machine learning or language acquisition, but its evolutionary roots may go back to the first organisms that were internally motivated to learn and represent their environment. Here, we study under what conditions such continuous learning (CL) may be more adaptive than simple reinforcement learning and examine how it could have evolved from the same basic associative elements. We use agent-based computer simulations to compare three learning strategies: simple reinforcement learning; reinforcement learning with chaining (RL-chain) and CL that applies the same associative mechanisms used by the other strategies, but also seeks statistical regularities in the relations among all items in the environment, regardless of the initial association with food. We show that a sufficiently structured environment favours the evolution of both RL-chain and CL and that CL outperforms the other strategies when food is relatively rare and the time for learning is limited. This advantage of internally motivated CL stems from its ability to capture statistical patterns in the environment even before they are associated with food, at which point they immediately become useful for planning.     

Impact of sharing full versus averaged social information on social influence and estimation accuracy

The recent developments of social networks and recommender systems have dramatically increased the amount of social information shared in human communities, challenging the human ability to process it. As a result, sharing aggregated forms of social information is becoming increasingly popular. However, it is unknown whether sharing aggregated information improves people’s judgments more than sharing the full available information. Here, we compare the performance of groups in estimation tasks when social information is fully shared versus when it is first averaged and then shared. We find that improvements in estimation accuracy are comparable in both cases. However, our results reveal important differences in subjects’ behaviour: (i) subjects follow the social information more when receiving an average than when receiving all estimates, and this effect increases with the number of estimates underlying the average; (ii) subjects follow the social information more when it is higher than their personal estimate than when it is lower. This effect is stronger when receiving all estimates than when receiving an average. We introduce a model that sheds light on these effects, and confirms their importance for explaining improvements in estimation accuracy in all treatments.     

Inferring social network structure in ecological systems from spatio-temporal data streams

We propose a methodology for extracting social network structure from spatio-temporal datasets that describe timestamped occurrences of individuals. Our approach identifies temporal regions of dense agent activity and links are drawn between individuals based on their co-occurrences across these ‘gathering events’. The statistical significance of these connections is then tested against an appropriate null model. Such a framework allows us to exploit the wealth of analytical and computational tools of network analysis in settings where the underlying connectivity pattern between interacting agents (commonly termed the adjacency matrix) is not given a priori. We perform experiments on two large-scale datasets (greater than 10⁶ points) of great tit Parus major wild bird foraging records and illustrate the use of this approach by examining the temporal dynamics of pairing behaviour, a process that was previously very hard to observe. We show that established pair bonds are maintained continuously, whereas new pair bonds form at variable times before breeding, but are characterized by a rapid development of network proximity. The method proposed here is general, and can be applied to any system with information about the temporal co-occurrence of interacting agents.