MIL-101(Fe) @TiO2 nanotube composite material is used for the solid phase extraction of non-steroidal anti-inflammatory drugs under the synergy of multiple interactions

Increasing the adsorption sites and effective interactions between sorbents and the targets can improve the solid-phase extraction (SPE) efficiency. Herein, based on the advantages of MOFs and TiO2 nanotubes (TiO2...

Review of phosphocholine substituents on bacterial pathogen glycans: Synthesis, structures and interactions with host proteins

Among the non-carbohydrate components of glycans, the addition of phosphocholine (ChoP) to the glycans of pathogens occurs more rarely than acetylation or methylation, but it has far more potent biological consequences. These arise from ChoP's multiple interactions with host proteins, which are important at all stages of the infection process. These stages include initial adherence to cells, encountering the host's innate immune system and then the adaptive immune system. Thus, in the initial stages of an infection, ChoP groups are an asset to the pathogen, but they can turn into a disadvantage subsequently. In this review, we have focussed on structural aspects of these phenomena. We describe the biosynthesis of the ChoP modification, the structures of the pathogen glycans known to carry ChoP groups and the host proteins that recognize ChoP.

Influence of Multiple Interactions in Value Cocreation on Sports Spectators’ Perceived Value of Sports Events

This study analyzed the effects of multiple interactions in value cocreation activities involving sports spectators. Interaction activities for value cocreation at sports events comprise spectator-athlete and spectator-staff interactions. A survey of spectators at the 2017 Wuhan Open revealed that spectator-athlete and spectator-staff interactions increased spectator perceived value, which in turn increased spectator satisfaction and loyalty. Spectator-staff interactions had a greater effect on spectator sports event value than did spectator-athlete interactions. Therefore, organizers of sports events should effectively manage multiple value cocreation interactions to improve spectator satisfaction and loyalty. The present study’s consideration of the effect of multiple interactions in value cocreation extends value cocreation theory.

Computational Screening of Isolated Compounds From Goniothalamus Species to Identify Potential Inhibitors for Dengue Virus

In this study, a set of 234 chemical constituents reported from Goniothalamus species were docked against envelope (E), NS2B/NS3, NS5 methyltransferase, and NS5 RdRp dengue virus (DENV) protein. As the result, compounds 95, 96, 97, 100, 149, 155, and 187 were identified as potential dengue protease inhibitors based on minimal docking energy values and multiple interactions with binding sites. The results from in-silico Lipinski’ rule and ADMET analysis showed that compound 149 was predicted as the most potential compound that fulfills the drug-likeness properties. Ligand 149 was found to be able to fit in well and remain stable in the binding site of proteins envelope, NS2B/NS3, NS5 methyltransferase and NS5 RdRp. The results from molecular dynamic simulations indicate that the ligand-protein complex of 149 in NS5 methyltransferase showed the most preferable, successfully interacted within the active sites and were able to reach convergence within 100 ns.

Improving De Novo Molecular Design with Curriculum Learning

Reinforcement learning (RL) is a powerful paradigm that has gained popularity across multiple domains. However, applying RL may come at a cost of multiple interactions between the agent and the environment. This cost can be especially pronounced when the single feedback from the environment is slow or computationally expensive, causing extensive periods of nonproductivity. Curriculum learning (CL) provides a suitable alternative by arranging a sequence of tasks of increasing complexity with the aim of reducing the overall cost of learning. Here, we demonstrate the application of CL for drug discovery. We implement CL in the de novo design platform, REINVENT, and apply it on illustrative de novo molecular design problems of different complexity. The results show both accelerated learning and a positive impact on the quality of the output when compared to standard policy based RL. To our knowledge, this is the first application of CL for the purposes of de novo molecular design. The code is freely available at https://github.com/MolecularAI/Reinvent.

The Advancing Roles of Exosomes in Breast Cancer

Breast cancer (BC) develops from breast tissue and is the most common aggressive malignant tumor in women worldwide. Although advanced treatment strategies have been applied and reduced current mortality rates, BC control remains unsatisfactory. It is essential to elucidate the underlying molecular mechanisms to assist clinical options. Exosomes are a type of extracellular vesicles and mediate cellular communications by delivering various biomolecules (oncogenes, oncomiRs, proteins, and even pharmacological compounds). These bioactive molecules can be transferred to change the transcriptome of target cells and influence tumor-related signaling pathways. Extensive studies have implicated exosomes in BC biology, including therapeutic resistance and the surrounding microenvironment. This review focuses on discussing the functions of exosomes in tumor treatment resistance, invasion and metastasis of BC. Moreover, we will also summarize multiple interactions between exosomes and the BC tumor microenvironment. Finally, we propose promising clinical applications of exosomes in BC.

HVEM structures and mutants reveal distinct functions of binding to LIGHT and BTLA/CD160

HVEM is a TNF (tumor necrosis factor) receptor contributing to a broad range of immune functions involving diverse cell types. It interacts with a TNF ligand, LIGHT, and immunoglobulin (Ig) superfamily members BTLA and CD160. Assessing the functional impact of HVEM binding to specific ligands in different settings has been complicated by the multiple interactions of HVEM and HVEM binding partners. To dissect the molecular basis for multiple functions, we determined crystal structures that reveal the distinct HVEM surfaces that engage LIGHT or BTLA/CD160, including the human HVEM–LIGHT–CD160 ternary complex, with HVEM interacting simultaneously with both binding partners. Based on these structures, we generated mouse HVEM mutants that selectively recognized either the TNF or Ig ligands in vitro. Knockin mice expressing these muteins maintain expression of all the proteins in the HVEM network, yet they demonstrate selective functions for LIGHT in the clearance of bacteria in the intestine and for the Ig ligands in the amelioration of liver inflammation.

Improving De Novo Molecular Design with Curriculum Learning

Reinforcement learning (RL) is a powerful paradigm that has gained popularity across multiple domains. However, applying RL may come at a cost of multiple interactions between the agent and the environment. This cost can be especially pronounced when the single feedback from the environment is slow or computationally expensive, causing extensive periods of nonproductivity. Curriculum learning (CL) provides a suitable alternative by arranging a sequence of tasks of increasing complexity with the aim of reducing the overall cost of learning. Here, we demonstrate the application of CL for drug discovery. We implement CL in the de novo design platform, REINVENT, and apply it on illustrative de novo molecular design problems of different complexity. The results show both accelerated learning and a positive impact on the quality of the output when compared to standard policy based RL. To our knowledge, this is the first application of CL for the purposes of de novo molecular design. The code is freely available at https://github.com/MolecularAI/Reinvent.

BIG DATA ANALYTICS FOR PREVENTIVE MAINTENANCE MANAGEMENT

Maintenance data for government buildings in Putrajaya, Malaysia, consists of a vast volume of data that is divided into different classes based on the functions of the maintenance tasks. As a result, multiple interactions from stakeholders and customers are required. This necessitates the collection of data that is specific to the stakeholders and customers. Big data can also forecast for predictive maintenance purposes in maintenance management. The current data practise relies solely on well-structured statistical data, resulting in static analysis and findings. Predictive maintenance under the Big Data idea will also use non-visible data such as social media and web search queries, which is a novel way to use Big Data analytics. The metamodel technique will be used in this study to evaluate the predictive maintenance model and faulty events in order to verify that the asset, facilities, and buildings are in excellent working order utilising systematic maintenance analytics. The metamodel method proposed a predictive maintenance procedure in Putrajaya by utilising the big data idea for maintenance management data.