EXAMPLE_QUESTIONS – Supplemental material for The commercialisation of Internet-opinion management: How the market is engaged in state control in China

Supplemental material, EXAMPLE_QUESTIONS for The commercialisation of Internet-opinion management: How the market is engaged in state control in China by Rui Hou in New Media & Society</p

paticipant_information – Supplemental material for The commercialisation of Internet-opinion management: How the market is engaged in state control in China

Supplemental material, paticipant_information for The commercialisation of Internet-opinion management: How the market is engaged in state control in China by Rui Hou in New Media & Society</p

Triphenyl Phosphate (TPHP)-Induced Neurotoxicity in Adult Male Chinese Rare Minnows (<i>Gobiocypris rarus</i>)

The neurotoxicity of triphenyl phosphate (TPHP) in exposed humans and laboratory animals is under debate. The rapid crossing of the blood-brain barrier (BBB) and high distribution of TPHP in fish brains have raised widespread concerns about potential neurotoxicity. Adult male Chinese rare minnows (Gobiocypris rarus) were used as a model and exposed to 0, 20, or 100 μg/L TPHP for 28 days. We evaluated the BBB permeability, neuroinflammatory response, cell proliferation and apoptosis, synaptic plasticity and synapse loss in fish brains via the learning/memory performance of fish following 28 days of TPHP exposure. TPHP significantly increased the BBB permeability, activated the neuroinflammatory response, and decreased the tight junction-related mRNA levels of claudin-5α and occludin in the fish brain. In addition, cell proliferation was inhibited by treatment with 100 μg/L TPHP, but no significant apoptosis was observed in the brain. Fish exposed to 100 μg/L TPHP exhibited significantly decreased dendritic arborization in pyramidal neurons in the cerebellum (Ce), and the maze test indicated impaired learning/memory performance. Taken together, these findings provide scientific evidence that TPHP is neurotoxic to fish and further suggest that TPHP may not be a safe alternative for aquatic organisms

Machine-Learning-Based Data Analysis Method for Cell-Based Selection of DNA-Encoded Libraries

DNA-encoded library (DEL) is a powerful ligand discovery technology that has been widely adopted in the pharmaceutical industry. DEL selections are typically performed with a purified protein target immobilized on a matrix or in solution phase. Recently, DELs have also been used to interrogate the targets in the complex biological environment, such as membrane proteins on live cells. However, due to the complex landscape of the cell surface, the selection inevitably involves significant nonspecific interactions, and the selection data are much noisier than the ones with purified proteins, making reliable hit identification highly challenging. Researchers have developed several approaches to denoise DEL datasets, but it remains unclear whether they are suitable for cell-based DEL selections. Here, we report the proof-of-principle of a new machine-learning (ML)-based approach to process cell-based DEL selection datasets by using a Maximum A Posteriori (MAP) estimation loss function, a probabilistic framework that can account for and quantify uncertainties of noisy data. We applied the approach to a DEL selection dataset, where a library of 7,721,415 compounds was selected against a purified carbonic anhydrase 2 (CA-2) and a cell line expressing the membrane protein carbonic anhydrase 12 (CA-12). The extended-connectivity fingerprint (ECFP)-based regression model using the MAP loss function was able to identify true binders and also reliable structure–activity relationship (SAR) from the noisy cell-based selection datasets. In addition, the regularized enrichment metric (known as MAP enrichment) could also be calculated directly without involving the specific machine-learning model, effectively suppressing low-confidence outliers and enhancing the signal-to-noise ratio. Future applications of this method will focus on de novo ligand discovery from cell-based DEL selections

Image_1_miR-193a-3p Mediates Placenta Accreta Spectrum Development by Targeting EFNB2 via Epithelial-Mesenchymal Transition Pathway Under Decidua Defect Conditions.TIFF

Objective: To clarify the role of microRNA-193a-3p (miR-193a-3p) in the pathogenesis of placenta accreta spectrum.Methods: The placental tissue expression levels of miR-193a-3p and Ephrin-B2 (EFNB2) were compared between a placenta accreta spectrum group and a control group. Transwell migration and invasion assays were used to verify the effect of miR-193a-3p and EFNB2 on HTR-8/SVneo cells cultured in human endometrial stromal cell (hESC)-conditioned medium. Epithelial-mesenchymal transition (EMT)-related proteins were examined by western blotting to establish whether the EMT pathway was altered in placenta accreta spectrum. To determine whether EFNB2 is a target gene of miR-193a-3p, luciferase activity assays were performed.Results: miR-193a-3p was upregulated but EFNB2 downregulated in the placenta accreta spectrum group and EFNB2 was a direct target of miR-193a-3p. Overexpression or inhibition of miR-193a-3p revealed that miR-193a-3p promoted the migration and invasion of HTR-8/SVneo cells cultured in hESC-conditioned medium. Furthermore, EMT was induced, as shown by increased N-cadherin, vimentin, MMP2, and MMP9 and decreased E-cadherin in the placenta accreta spectrum group and in HTR-8/SVneo cells transfected with miR-193a-3p mimics or si-EFNB2. The negative effect of miR-193a-3p inhibitor was reversed by co-transfection with si-EFNB2 in function studies and in analyses of EMT-related proteins in vitro.Conclusion: miR-193a-3p which upregulated in placenta accreta spectrum group increases HTR-8/SVneo cell migration and invasion by targeting EFNB2 via the EMT pathway under decidua defect conditions to lead to placenta accreta spectrum.</p

Machine-Learning-Based Data Analysis Method for Cell-Based Selection of DNA-Encoded Libraries

DNA-encoded library (DEL) is a powerful ligand discovery technology that has been widely adopted in the pharmaceutical industry. DEL selections are typically performed with a purified protein target immobilized on a matrix or in solution phase. Recently, DELs have also been used to interrogate the targets in the complex biological environment, such as membrane proteins on live cells. However, due to the complex landscape of the cell surface, the selection inevitably involves significant nonspecific interactions, and the selection data are much noisier than the ones with purified proteins, making reliable hit identification highly challenging. Researchers have developed several approaches to denoise DEL datasets, but it remains unclear whether they are suitable for cell-based DEL selections. Here, we report the proof-of-principle of a new machine-learning (ML)-based approach to process cell-based DEL selection datasets by using a Maximum A Posteriori (MAP) estimation loss function, a probabilistic framework that can account for and quantify uncertainties of noisy data. We applied the approach to a DEL selection dataset, where a library of 7,721,415 compounds was selected against a purified carbonic anhydrase 2 (CA-2) and a cell line expressing the membrane protein carbonic anhydrase 12 (CA-12). The extended-connectivity fingerprint (ECFP)-based regression model using the MAP loss function was able to identify true binders and also reliable structure–activity relationship (SAR) from the noisy cell-based selection datasets. In addition, the regularized enrichment metric (known as MAP enrichment) could also be calculated directly without involving the specific machine-learning model, effectively suppressing low-confidence outliers and enhancing the signal-to-noise ratio. Future applications of this method will focus on de novo ligand discovery from cell-based DEL selections

Machine-Learning-Based Data Analysis Method for Cell-Based Selection of DNA-Encoded Libraries

DNA-encoded library (DEL) is a powerful ligand discovery technology that has been widely adopted in the pharmaceutical industry. DEL selections are typically performed with a purified protein target immobilized on a matrix or in solution phase. Recently, DELs have also been used to interrogate the targets in the complex biological environment, such as membrane proteins on live cells. However, due to the complex landscape of the cell surface, the selection inevitably involves significant nonspecific interactions, and the selection data are much noisier than the ones with purified proteins, making reliable hit identification highly challenging. Researchers have developed several approaches to denoise DEL datasets, but it remains unclear whether they are suitable for cell-based DEL selections. Here, we report the proof-of-principle of a new machine-learning (ML)-based approach to process cell-based DEL selection datasets by using a Maximum A Posteriori (MAP) estimation loss function, a probabilistic framework that can account for and quantify uncertainties of noisy data. We applied the approach to a DEL selection dataset, where a library of 7,721,415 compounds was selected against a purified carbonic anhydrase 2 (CA-2) and a cell line expressing the membrane protein carbonic anhydrase 12 (CA-12). The extended-connectivity fingerprint (ECFP)-based regression model using the MAP loss function was able to identify true binders and also reliable structure–activity relationship (SAR) from the noisy cell-based selection datasets. In addition, the regularized enrichment metric (known as MAP enrichment) could also be calculated directly without involving the specific machine-learning model, effectively suppressing low-confidence outliers and enhancing the signal-to-noise ratio. Future applications of this method will focus on de novo ligand discovery from cell-based DEL selections

Machine-Learning-Based Data Analysis Method for Cell-Based Selection of DNA-Encoded Libraries

DNA-encoded library (DEL) is a powerful ligand discovery technology that has been widely adopted in the pharmaceutical industry. DEL selections are typically performed with a purified protein target immobilized on a matrix or in solution phase. Recently, DELs have also been used to interrogate the targets in the complex biological environment, such as membrane proteins on live cells. However, due to the complex landscape of the cell surface, the selection inevitably involves significant nonspecific interactions, and the selection data are much noisier than the ones with purified proteins, making reliable hit identification highly challenging. Researchers have developed several approaches to denoise DEL datasets, but it remains unclear whether they are suitable for cell-based DEL selections. Here, we report the proof-of-principle of a new machine-learning (ML)-based approach to process cell-based DEL selection datasets by using a Maximum A Posteriori (MAP) estimation loss function, a probabilistic framework that can account for and quantify uncertainties of noisy data. We applied the approach to a DEL selection dataset, where a library of 7,721,415 compounds was selected against a purified carbonic anhydrase 2 (CA-2) and a cell line expressing the membrane protein carbonic anhydrase 12 (CA-12). The extended-connectivity fingerprint (ECFP)-based regression model using the MAP loss function was able to identify true binders and also reliable structure–activity relationship (SAR) from the noisy cell-based selection datasets. In addition, the regularized enrichment metric (known as MAP enrichment) could also be calculated directly without involving the specific machine-learning model, effectively suppressing low-confidence outliers and enhancing the signal-to-noise ratio. Future applications of this method will focus on de novo ligand discovery from cell-based DEL selections

Data_Sheet_1_miR-193a-3p Mediates Placenta Accreta Spectrum Development by Targeting EFNB2 via Epithelial-Mesenchymal Transition Pathway Under Decidua Defect Conditions.PDF

Objective: To clarify the role of microRNA-193a-3p (miR-193a-3p) in the pathogenesis of placenta accreta spectrum.Methods: The placental tissue expression levels of miR-193a-3p and Ephrin-B2 (EFNB2) were compared between a placenta accreta spectrum group and a control group. Transwell migration and invasion assays were used to verify the effect of miR-193a-3p and EFNB2 on HTR-8/SVneo cells cultured in human endometrial stromal cell (hESC)-conditioned medium. Epithelial-mesenchymal transition (EMT)-related proteins were examined by western blotting to establish whether the EMT pathway was altered in placenta accreta spectrum. To determine whether EFNB2 is a target gene of miR-193a-3p, luciferase activity assays were performed.Results: miR-193a-3p was upregulated but EFNB2 downregulated in the placenta accreta spectrum group and EFNB2 was a direct target of miR-193a-3p. Overexpression or inhibition of miR-193a-3p revealed that miR-193a-3p promoted the migration and invasion of HTR-8/SVneo cells cultured in hESC-conditioned medium. Furthermore, EMT was induced, as shown by increased N-cadherin, vimentin, MMP2, and MMP9 and decreased E-cadherin in the placenta accreta spectrum group and in HTR-8/SVneo cells transfected with miR-193a-3p mimics or si-EFNB2. The negative effect of miR-193a-3p inhibitor was reversed by co-transfection with si-EFNB2 in function studies and in analyses of EMT-related proteins in vitro.Conclusion: miR-193a-3p which upregulated in placenta accreta spectrum group increases HTR-8/SVneo cell migration and invasion by targeting EFNB2 via the EMT pathway under decidua defect conditions to lead to placenta accreta spectrum.</p

Machine-Learning-Based Data Analysis Method for Cell-Based Selection of DNA-Encoded Libraries

DNA-encoded library (DEL) is a powerful ligand discovery technology that has been widely adopted in the pharmaceutical industry. DEL selections are typically performed with a purified protein target immobilized on a matrix or in solution phase. Recently, DELs have also been used to interrogate the targets in the complex biological environment, such as membrane proteins on live cells. However, due to the complex landscape of the cell surface, the selection inevitably involves significant nonspecific interactions, and the selection data are much noisier than the ones with purified proteins, making reliable hit identification highly challenging. Researchers have developed several approaches to denoise DEL datasets, but it remains unclear whether they are suitable for cell-based DEL selections. Here, we report the proof-of-principle of a new machine-learning (ML)-based approach to process cell-based DEL selection datasets by using a Maximum A Posteriori (MAP) estimation loss function, a probabilistic framework that can account for and quantify uncertainties of noisy data. We applied the approach to a DEL selection dataset, where a library of 7,721,415 compounds was selected against a purified carbonic anhydrase 2 (CA-2) and a cell line expressing the membrane protein carbonic anhydrase 12 (CA-12). The extended-connectivity fingerprint (ECFP)-based regression model using the MAP loss function was able to identify true binders and also reliable structure–activity relationship (SAR) from the noisy cell-based selection datasets. In addition, the regularized enrichment metric (known as MAP enrichment) could also be calculated directly without involving the specific machine-learning model, effectively suppressing low-confidence outliers and enhancing the signal-to-noise ratio. Future applications of this method will focus on de novo ligand discovery from cell-based DEL selections