Superalloys fracture process inference based on overlap analysis of 3D models.

Superalloy materials exhibit susceptibility to fracture failures stemming from the influence of thermomechanical factors. To comprehensively understand the fracture mechanisms, material properties, root causes of failure, and the subsequent optimization of alloys, a detailed analysis of the internal fracture process and the morphological traits of the fracture surface is imperative. Traditional analysis of fracture surfaces solely relies on 2D images, thus lacking crucial 3D information.

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 + Cr 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.

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) .

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.

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

Based on the use of quercetin for treating diabetes and H S for promoting wound healing, a series of three quercetin-linker-H S donor conjugates was designed, synthesized and characterized by H-NMR, 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.

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.

Modeling the Evolution of Biological Neural Networks Based on 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 hermaphrodites at different postembryonic ages, from birth to adulthood.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.