Experimental Evaluation of Performance of a Low-Initial-Viscosity Gel Flooding System.

In order to effectively adjust reservoir heterogeneity and further exploit the remaining oil, a new type of low-viscosity gel was prepared by adding a regulating agent, retarder, and reinforcing agent on the basis of a polymer + Cr3+ crosslinking system. The new gel has the advantages of low initial viscosity, a slow gel formation rate, and high strength after gel formation. The effectiveness of the gel was verified through three-layer core displacement experiments, and the injection scheme was optimized by changing the slug combination of the polymer and the gel. The results showed that the gel can effectively block the high-permeability layer and adjust reservoir heterogeneity. An injection of 0.1 pore volume (PV) low-initial-viscosity gel can improve oil recovery by 5.10%. By changing the slug combination of the gel and polymer, oil recovery was further increased by 3.12% when using an injection of 0.07 PV low-initial-viscosity gel +0.2 PV high-concentration polymer +0.05 PV low-initial-viscosity gel +0.5 PV high-concentration polymer.

Design, Synthesis, Biological Evaluation and Molecular Docking Studies of Quercetin-Linker-H2 S Donor Conjugates for the Treatment of Diabetes and Wound Healing.

Based on the use of quercetin for treating diabetes and H2 S for promoting wound healing, a series of three quercetin-linker-H2 S donor conjugates was designed, synthesized and characterized by 1 H-NMR, 13 C-NMR and MS. Meanwhile, in vitro evaluation of these compounds was also researched by IR-HepG2 treatment experiment, MTT assay, scratch test and tubule formation experiment. The three compounds could be used to treat insulin resistance induced by high glucose and promote the proliferation of human umbilical vein endothelial cells, wound healing, and the formation of tubules in vitro under a high-glucose environment. Our results illustrate that these compounds could be used to treat diabetes and promote wound healing at the same time. Furthermore, molecular docking study results of the compounds were consistent with the evaluated biological activity. In vivo research of compounds is underway.

Discovery and preclinical evaluations of GST-HG131, a novel HBV antigen inhibitor for the treatment of chronic hepatitis B infection.

Chronic hepatitis B (CHB) remains a significant health challenge worldwide. The current treatments for CHB achieve less than 10% cure rates, majority of the patients are on therapy for life. Therefore, cure of CHB is a high unmet medical need. HBV surface antigen (HBsAg) loss and seroconversion are considered as the key for the cure. RG7834 is a novel, orally bioavailable small molecule reported to reduce HBV antigens. Based on RG7834 chemistry, we designed and discovered a series of dihydrobenzopyridooxazepine (DBP) series of HBV antigen inhibitors. Extensive SAR studies led us to GST-HG131 with excellent reduction of HBV antigens (both HBsAg and HBeAg) in vitro and in vivo. GST-HG131 improved safety in rat toxicology studies over RG7834. The promising inhibitory activity, together with animal safety enhancement, merited GST-HG131 progressed into clinical development in 2020 (NCT04499443).

A tensor-based framework for studying eigenvector multicentrality in multilayer networks.

Centrality is widely recognized as one of the most critical measures to provide insight into the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively.

A New 7-Azaindole Structure Analog: Molecular Docking, Synthesis and Preliminary Biological Activity in Vitro for Anticancer.

In this work, a series of 7-azaindole analogs were designed by the bioisosteric principle based on the pharmacodynamic parent nucleus. Moreover, 5-[(5-chloro-1H-pyrrolo[2,3-b]pyridin-3-yl)methyl]-N-{[6-(trifluoromethyl)pyridin-3-yl]methyl}pyrimidin-2-amine (compound P1) with the strongest interaction with colony-stimulating factor 1 receptor (CSF-1R) was screened by molecular docking. Compound P1 was successfully prepared by the six-step reaction with HPLC purity of 99.26 % and characterized by 1 H-NMR and ESI-MS spectra. In vitro bioactivity study showed that compound P1 appeared the cytotoxicity to MCF-7 and A549 cells, especially to HOS cells (IC50 =88.79±8.07 nM), while it had lower toxicity to normal L929 cells (IC50 =140.49±8.03 µM). In addition, compound P1 could induce HOS cell death by apoptosis and blocking the G0/G1 phase at nanomolar concentrations. The obtained results indicated that compound P1 might be a promising candidate compound for anticancer drug.

Modeling the Evolution of Biological Neural Networks Based on Caenorhabditis elegans Connectomes across Development.

Knowledge of the structural properties of biological neural networks can help in understanding how particular responses and actions are generated. Recently, Witvliet et al. published the connectomes of eight isogenic Caenorhabditis elegans hermaphrodites at different postembryonic ages, from birth to adulthood. We analyzed the basic structural properties of these biological neural networks. From birth to adulthood, the asymmetry between in-degrees and out-degrees over the C. elegans neuronal network increased with age, in addition to an increase in the number of nodes and edges. The degree distributions were neither Poisson distributions nor pure power-law distributions. We have proposed a model of network evolution with different initial attractiveness for in-degrees and out-degrees of nodes and preferential attachment, which reproduces the asymmetry between in-degrees and out-degrees and similar degree distributions via the tuning of the initial attractiveness values. In this study, we present the well-preserved structural properties of C. elegans neuronal networks across development, and provide some insight into understanding the evolutionary processes of biological neural networks through a simple network model.

Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees.

With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to their development. Therefore, outsourcing the encrypted health data to the cloud has been an appealing strategy. However, date aggregation will become difficult. Some recently-proposed schemes try to address this problem. However, there are still some functions and privacy issues that are not discussed. In this paper, we propose a privacy-enhanced and multifunctional health data aggregation scheme (PMHA-DP) under differential privacy. Specifically, we achieve a new aggregation function, weighted average (WAAS), and design a privacy-enhanced aggregation scheme (PAAS) to protect the aggregated data from cloud servers. Besides, a histogram aggregation scheme with high accuracy is proposed. PMHA-DP supports fault tolerance while preserving data privacy. The performance evaluation shows that the proposal leads to less communication overhead than the existing one.

Photodynamic antibacterial research on hypericin-loaded PEGylated mesoporous silica delivery system.

In this study, a novel drug delivery system (MSN-PEG-Hypericin) was successfully fabricated using tetraethyl orthosilicate and 3-aminopropyltriethoxysilane as raw materials, and the PEGylation of the prepared aminated mesoporous silica and grafting of hypericin onto the carrier were further conducted to obtain MSN-PEG-Hypericin. The successful preparation of MSN-PEG-Hypericin was characterized by several physical-chemical techniques. Furthermore, the MSN-PEG-Hypericin system increased the ability of hypericin to generate reactive oxygen species (ROS) in vitro. The cytotoxicity assay and hemolysis analysis showed that MSN-PEG-Hypericin had good biocompatibility. For antibacterial studies, the irradiation time and incubation time of photodynamic therapy (PDT) for S. aureus and E. coli were respectively 8 min and 8 h, and the concentrations of hypericin were 2.5 and 5 µg/mL. The result of triphenyl tetrazolium chloride assay indicated that MSN-PEG-Hypericin had stronger photodynamic antibacterial activity than free hypericin, and S. aureus was more sensitive to PDT than E. coli, which was related to their cell structural differences. The antibacterial mechanism study indicated that the generated ROS could destroy the bacterial structures and cause bacterial death due to the leakage of the contents. The MSN-PEG-Hypericin system prepared in this study had potential application prospects in the antibacterial field.

The Intrinsic Similarity of Topological Structure in Biological Neural Networks.

Most previous studies mainly have focused on the analysis of structural properties of individual neuronal networks from C. elegans. In recent years, an increasing number of synapse-level neural maps, also known as biological neural networks, have been reconstructed. However, it is not clear whether there are intrinsic similarities of structural properties of biological neural networks from different brain compartments or species. To explore this issue, we collected nine connectomes at synaptic resolution including C. elegans, and analyzed their structural properties. We found that these biological neural networks possess small-world properties and modules. Excluding the Drosophila larval visual system, these networks have rich clubs. The distributions of synaptic connection strength for these networks can be fitted by the truncated pow-law distributions. Additionally, compared with the power-law model, a log-normal distribution is a better model to fit the complementary cumulative distribution function (CCDF) of degree for these neuronal networks. Moreover, we also observed that these neural networks belong to the same superfamily based on the significance profile (SP) of small subgraphs in the network. Taken together, these findings suggest that biological neural networks share intrinsic similarities in their topological structure, revealing some principles underlying the formation of biological neural networks within and across species.

A general urban spreading pattern of COVID-19 and its underlying mechanism.

Currently, the global situation of COVID-19 is aggravating, pressingly calling for efficient control and prevention measures. Understanding the spreading pattern of COVID-19 has been widely recognized as a vital step for implementing non-pharmaceutical measures. Previous studies explained the differences in contagion rates due to the urban socio-political measures, while fine-grained geographic urban spreading pattern still remains an open issue. Here, we fill this gap by leveraging the trajectory data of 197,808 smartphone users (including 17,808 anonymous confirmed cases) in nine cities in China. We find a general spreading pattern in all cities: the spatial distribution of confirmed cases follows a power-law-like model and the spreading centroid human mobility is time-invariant. Moreover, we reveal that long average traveling distance results in a high growth rate of spreading radius and wide spatial diffusion of confirmed cases in the fine-grained geographic model. With such insight, we adopt the Kendall model to simulate the urban spreading of COVID-19 which can well fit the real spreading process. Our results unveil the underlying mechanism behind the spatial-temporal urban evolution of COVID-19, and can be used to evaluate the performance of mobility restriction policies implemented by many governments and to estimate the evolving spreading situation of COVID-19.

Discrimination reveals reconstructability of multiplex networks from partial observations.

An excellent method for predicting links in multiplex networks is reflected in its ability to reconstruct them accurately. Although link prediction methods perform well on estimating the existence probability of each potential link in monoplex networks by the set of partially observed links, we lack a mathematical tool to reconstruct the multiplex network from the observed aggregate topology and partially observed links in multiplex networks. Here, we fill this gap by developing a theoretical and computational framework that builds a probability space containing possible structures with a maximum likelihood estimation. Then, we discovered that the discrimination, an indicator quantifying differences between layers from an entropy perspective, determines the reconstructability, i.e., the accuracy of such reconstruction. This finding enables us to design the optimal strategy to allocate the set of observed links in different layers for promoting the optimal reconstruction of multiplex networks. Finally, the theoretical analyses are corroborated by empirical results from biological, social, engineered systems, and a large volume of synthetic networks.

Use of temporal contact graphs to understand the evolution of COVID-19 through contact tracing data.

Digital contact tracing has been recently advocated by China and many countries as part of digital prevention measures on COVID-19. Controversies have been raised about their effectiveness in practice as it remains open how they can be fully utilized to control COVID-19. In this article, we show that an abundance of information can be extracted from digital contact tracing for COVID-19 prevention and control. Specifically, we construct a temporal contact graph that quantifies the daily contacts between infectious and susceptible individuals by exploiting a large volume of location-related data contributed by 10,527,737 smartphone users in Wuhan, China. The temporal contact graph reveals five time-varying indicators can accurately capture actual contact trends at population level, demonstrating that travel restrictions (e.g., city lockdown) in Wuhan played an important role in containing COVID-19. We reveal a strong correlation between the contacts level and the epidemic size, and estimate several significant epidemiological parameters (e.g., serial interval). We also show that user participation rate exerts higher influence on situation evaluation than user upload rate does, indicating a sub-sampled dataset would be as good at prediction. At individual level, however, the temporal contact graph plays a limited role, since the behavior distinction between the infected and uninfected individuals are not substantial. The revealed results can tell the effectiveness of digital contact tracing against COVID-19, providing guidelines for governments to implement interventions using information technology.

Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types.

Despite extensive efforts to generate and analyze reference genomes, genetic models to predict gene regulation and cell fate decisions are lacking for most species. Here, we generated whole-body single-cell transcriptomic landscapes of zebrafish, Drosophila and earthworm. We then integrated cell landscapes from eight representative metazoan species to study gene regulation across evolution. Using these uniformly constructed cross-species landscapes, we developed a deep-learning-based strategy, Nvwa, to predict gene expression and identify regulatory sequences at the single-cell level. We systematically compared cell-type-specific transcription factors to reveal conserved genetic regulation in vertebrates and invertebrates. Our work provides a valuable resource and offers a new strategy for studying regulatory grammar in diverse biological systems.

A unified drug-target interaction prediction framework based on knowledge graph and recommendation system.

Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.

Resilience centrality in complex networks.

Resilience describes a system's ability to adjust its activity to retain the basic functionality when errors or failures occur in components (nodes) of the network. Due to the complexity of a system's structure, different components in the system exhibit diversity in the ability to affect the resilience of the system, bringing us a great challenge to protect the system from collapse. A fundamental problem is therefore to propose a physically insightful centrality index, with which to quantify the resilience contribution of a node in any systems effectively. However, existing centrality indexes are not suitable for the problem because they only consider the network structure of the system and ignore the impact of underlying dynamic characteristics. To break the limits, we derive a new centrality index: resilience centrality from the 1D dynamic equation of systems, with which we can quantify the ability of nodes to affect the resilience of the system accurately. Resilience centrality unveils the long-sought relations between the ability of nodes in a system's resilience and network structure of the system: the capacity is mainly determined by the degree and weighted nearest-neighbor degree of the node, in which weighted nearest-neighbor degree plays a prominent role. Further, we demonstrate that weighted nearest-neighbor degree has a positive impact on resilience centrality, while the effect of the degree depends on a specific parameter, average weighted degree ß_{eff}, in the 1D dynamic equation. To test the performance of our approach, we construct four real networks from data, which corresponds to two complex systems with entirely different dynamic characteristics. The simulation results demonstrate the effectiveness of our resilience centrality, providing us theoretical insights into the protection of complex systems from collapse.

Preparation and Experimental Study of a Low-Initial-Viscosity Gel Plugging Agent.

The development of dominant seepage channels after polymer flooding makes it more difficult to effectively exploit reservoirs, and gel plugging technology is an effective method to solve this problem. However, conventional gels experience problems such as high initial viscosity and they easily contaminate the medium- and low-permeability layers. Therefore, a low-initial-viscosity gel plugging agent is proposed in this paper. By optimizing the concentration of polymer, cross-linking agent, and other functional auxiliaries, the best gel formulation was obtained. To test the plugging ability of the gel system on the core and its oil displacement effect, a plugging performance test experiment and three-tube core parallel oil displacement experiment were performed. The research results showed that the best formulation of gel plugging agent is as follows: 500-1000 mg/L polymer LH2500, 1000-2500 mg/L cross-linking agent CYJL, 200-500 mg/L citric acid, 100-150 mg/L sodium sulfite, and 100-200 mg/L sodium polyphosphate; its initial viscosity is less than 10 mPa·s, the gelation time is controllable within 10 to 40 days, and the gelation viscosity is above 2000 mPa·s. Core flooding experiments showed that the gel system has good core plugging performance, and the plugging rate of water from 0.48 to 3.9 µm2 is more than 99%; for the secondary polymer flooding reservoir, the recovery factor can be increased by 13.6% after plugging with 0.1 PV gel. At present, the gel has been successfully used in field tests and provides good oil increase and water control effects.

Design, Synthesis, and Evaluation of Tetrahydropyrrolo[1,2-c]pyrimidines as Capsid Assembly Inhibitors for HBV Treatment.

The discovery of novel tetrahydropyrrolo[1,2-c]pyrimidines derivatives from Bay41_4109 as hepatitis B virus (HBV) inhibitors is herein reported. The structure-activity relationship optimization led to one highly efficacious compound 28a (IC50 = 10 nM) with good PK profiles and the favorite L/P ratio. The hydrodynamic injection model in mice clearly demonstrated the efficacy of 28a against HBV replication.