The maximum capability of a topological feature in link prediction.

Networks offer a powerful approach to modeling complex systems by representing the underlying set of pairwise interactions. Link prediction is the task that predicts links of a network that are not directly visible, with profound applications in biological, social, and other complex systems. Despite intensive utilization of the topological feature in this task, it is unclear to what extent a feature can be leveraged to infer missing links. Here, we aim to unveil the capability of a topological feature in link prediction by identifying its prediction performance upper bound. We introduce a theoretical framework that is compatible with different indexes to gauge the feature, different prediction approaches to utilize the feature, and different metrics to quantify the prediction performance. The maximum capability of a topological feature follows a simple yet theoretically validated expression, which only depends on the extent to which the feature is held in missing and nonexistent links. Because a family of indexes based on the same feature shares the same upper bound, the potential of all others can be estimated from one single index. Furthermore, a feature's capability is lifted in the supervised prediction, which can be mathematically quantified, allowing us to estimate the benefit of applying machine learning algorithms. The universality of the pattern uncovered is empirically verified by 550 structurally diverse networks. The findings have applications in feature and method selection, and shed light on network characteristics that make a topological feature effective in link prediction.

Flexible temperature-pressure dual sensor based on 3D spiral thermoelectric Bi2Te3 films.

Dual-parameter pressure-temperature sensors are widely employed in personal health monitoring and robots to detect external signals. Herein, we develop a flexible composite dual-parameter pressure-temperature sensor based on three-dimensional (3D) spiral thermoelectric Bi2Te3 films. The film has a (000l) texture and good flexibility, exhibiting a maximum Seebeck coefficient of -181 µV K-1 and piezoresistance gauge factor of approximately -9.2. The device demonstrates a record-high temperature-sensing performance with a high sensing sensitivity (-426.4 µV K-1) and rapid response time (~0.95 s), which are better than those observed in most previous studies. In addition, owing to the piezoresistive effect in the Bi2Te3 film, the 3D-spiral deviceexhibits significant pressure-response properties with a pressure-sensing sensitivity of 120 Pa-1. This innovative approach achieves high-performance dual-parameter sensing using one kind of material with high flexibility, providing insight into the design and fabrication of many applications, such as e-skin.

A generalized linear threshold model for an improved description of the spreading dynamics.

Many spreading processes in our real-life can be considered as a complex contagion, and the linear threshold (LT) model is often applied as a very representative model for this mechanism. Despite its intensive usage, the LT model suffers several limitations in describing the time evolution of the spreading. First, the discrete-time step that captures the speed of the spreading is vaguely defined. Second, the synchronous updating rule makes the nodes infected in batches, which cannot take individual differences into account. Finally, the LT model is incompatible with existing models for the simple contagion. Here, we consider a generalized linear threshold (GLT) model for the continuous-time stochastic complex contagion process that can be efficiently implemented by the Gillespie algorithm. The time in this model has a clear mathematical definition, and the updating order is rigidly defined. We find that the traditional LT model systematically underestimates the spreading speed and the randomness in the spreading sequence order. We also show that the GLT model works seamlessly with the susceptible-infected or susceptible-infected-recovered model. One can easily combine them to model a hybrid spreading process in which simple contagion accumulates the critical mass for the complex contagion that leads to the global cascades. Overall, the GLT model we proposed can be a useful tool to study complex contagion, especially when studying the time evolution of the spreading.

Measuring similarity in co-occurrence data using ego-networks.

The co-occurrence association is widely observed in many empirical data. Mining the information in co-occurrence data is essential for advancing our understanding of systems such as social networks, ecosystems, and brain networks. Measuring similarity of entities is one of the important tasks, which can usually be achieved using a network-based approach. Here, we show that traditional methods based on the aggregated network can bring unwanted indirect relationships. To cope with this issue, we propose a similarity measure based on the ego network of each entity, which effectively considers the change of an entity's centrality from one ego network to another. The index proposed is easy to calculate and has a clear physical meaning. Using two different data sets, we compare the new index with other existing ones. We find that the new index outperforms the traditional network-based similarity measures, and it can sometimes surpass the embedding method. In the meanwhile, the measure by the new index is weakly correlated with those by other methods, hence providing a different dimension to quantify similarities in co-occurrence data. Altogether, our work makes an extension in the network-based similarity measure and can be potentially applied in several related tasks.

Multifunctional Nanoparticles for Multimodal Imaging-Guided Low-Intensity Focused Ultrasound/Immunosynergistic Retinoblastoma Therapy.

Retinoblastoma (RB) is prone to delayed diagnosis or treatment and has an increased likelihood of metastasizing. Thus, it is crucial to perform an effective imaging examination and provide optimal treatment of RB to prevent metastasis. Nanoparticles that support diagnostic imaging and targeted therapy are expected to noninvasively integrate tumor diagnosis and treatment. Herein, we report a multifunctional nanoparticle for multimodal imaging-guided low-intensity focused ultrasound (LIFU)/immunosynergistic RB therapy. Magnetic hollow mesoporous gold nanocages (AuNCs) conjugated with Fe3O4 nanoparticles (AuNCs-Fe3O4) were prepared to encapsulate muramyl dipeptide (MDP) and perfluoropentane (PFP). The multimodal imaging capabilities, antitumor effects, and dendritic cell (DC) activation capacity of these nanoparticles combined with LIFU were explored in vitro and in vivo. The biosafety of AuNCs-Fe3O4/MDP/PFP was also evaluated systematically. The multifunctional magnetic nanoparticles enhanced photoacoustic (PA), ultrasound (US), and magnetic resonance (MR) imaging in vivo and in vitro, which was helpful for diagnosis and efficacy evaluation. Upon accumulation in tumors via a magnetic field, the nanoparticles underwent phase transition under LIFU irradiation and MDP was released. A combined effect of AuNCs-Fe3O4/MDP/PFP and LIFU was recorded and verified. AuNCs-Fe3O4/MDP/PFP enhanced the therapeutic effect of LIFU and led to direct apoptosis/necrosis of tumors, while MDP promoted DC maturation and activation and activated the ability of DCs to recognize and clear tumor cells. By enhancing PA/US/MR imaging and inhibiting tumor growth, the multifunctional AuNC-Fe3O4/MDP/PFP nanoparticles show great potential for multimodal imaging-guided LIFU/immunosynergistic therapy of RB. The proposed nanoplatform facilitates cancer theranostics with high biosafety.

Magnetic-responsive and targeted cancer nanotheranostics by PA/MR bimodal imaging-guided photothermally triggered immunotherapy.

While theranostic nanoparticle (TNP)-based photothermal therapy (PTT) exhibits prominent promise for cancer therapy, metastatic cancers remain one of the main obstacles of effective PTT. Immunotherapy has been developed vigorously to inhibit metastatic cancers, but the heterogeneity of patients and the complexities of manufacturing cancer vaccines significantly hinder its further clinical applications. Herein, a photothermally triggered immunotherapeutic paradigm under imaging guidance was designed based on magnetic-responsive immunostimulatory nanoagents (MINPs) loaded with superparamagnetic iron oxide (SPIO) nanoparticles and cytosine-phosphate-guanine oligodeoxynucleotides (CpG ODNs). The fabricated MINPs with the clinically approved components acted not only as a contrast agent for photoacoustic (PA)/magnetic resonance (MR) bimodal imaging but also as a magnetic-targeting therapeutic agent for photothermally triggered immunotherapy. Under external magnetic fields, the MINPs showed a great magnetic-targeting ability, leading to high accumulation of the photoabsorber (SPIO) and the immunoadjuvant (CpG ODNs) in the tumors for precise bimodal imaging guidance. More importantly, the excellent photothermal conversion effect of the MINPs upon near-infrared (NIR) exposure enabled the effective photothermal destruction of the primary tumors, releasing tumor-associated antigens and showing 'autologous cancer vaccine'-like functions, thus activating robust antitumor immune responses, especially in the presence of CpG ODN-containing immunostimulatory nanoagents. Such generated immune responses can further attack the remaining tumors and distant metastatic tumors in mice. This work provides an imaging-guided photothermally triggered immunotherapeutic strategy based on multifunctional MINPs to effectively eliminate primary tumors and inhibit metastatic tumors simultaneously with high specificity, easy maneuverability and favorable biocompatibility. This strategy may potentially be applicable for precise individualized diagnosis and therapy of various tumors.