Evaluation of primary-level credit environment, indicator system and empirical analysis: A case study of credit construction in China county and district.

Townships (towns, streets) represent the foundational layer of China's administrative structure, and the quality of their credit environment is crucial for underpinning the development of a primary-level social credit system. This initiative aims to accelerate the establishment of the social credit system and cultivate a trustworthy economic and social environment. Starting from the three major fields of government, business and society, and focusing on integrity culture and credit innovation, the article proposes an innovative evaluation framework for primary-level credit environment and it can become a point of reference as a policy tool in international evaluation programs. Using clustering and the coefficient of variation methods, we quantitatively refine our indicator system, establishing a set of criteria to assess the primary-level credit environment. We incorporate hierarchical analysis, the entropy weight method, and machine learning models to conduct a comprehensive evaluation of the credit environments within 24 townships (towns, streets) of Fuyang District in Hangzhou City for the year 2023. The findings underscore the need for a realistic appraisal of the current state and deficiencies of the primary-level credit environment. We advocate for the bolstering of credit development within governmental, business, and societal realms. It's imperative to leverage the normative influence of honesty and integrity culture, enhance the breadth and application of credit innovations, and thereby foster the high-quality growth of the primary-level social credit system.

FARS2 Deficiency Causes Cardiomyopathy by Disrupting Mitochondrial Homeostasis and the Mitochondrial Quality Control System.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is a common heritable heart disease. Although HCM has been reported to be associated with many variants of genes involved in sarcomeric protein biomechanics, pathogenic genes have not been identified in patients with partial HCM. FARS2 (the mitochondrial phenylalanyl-tRNA synthetase), a type of mitochondrial aminoacyl-tRNA synthetase, plays a role in the mitochondrial translation machinery. Several variants of FARS2 have been suggested to cause neurological disorders; however, FARS2-associated diseases involving other organs have not been reported. We identified FARS2 as a potential novel pathogenic gene in cardiomyopathy and investigated its effects on mitochondrial homeostasis and the cardiomyopathy phenotype. METHODS: FARS2 variants in patients with HCM were identified using whole-exome sequencing, Sanger sequencing, molecular docking analyses, and cell model investigation. Fars2 conditional mutant (p.R415L) or knockout mice, fars2-knockdown zebrafish, and Fars2-knockdown neonatal rat ventricular myocytes were engineered to construct FARS2 deficiency models both in vivo and in vitro. The effects of FARS2 and its role in mitochondrial homeostasis were subsequently evaluated using RNA sequencing and mitochondrial functional analyses. Myocardial tissues from patients were used for further verification. RESULTS: We identified 7 unreported FARS2 variants in patients with HCM. Heart-specific Fars2-deficient mice presented cardiac hypertrophy, left ventricular dilation, progressive heart failure accompanied by myocardial and mitochondrial dysfunction, and a short life span. Heterozygous cardiac-specific Fars2R415L mice displayed a tendency to cardiac hypertrophy at age 4 weeks, accompanied by myocardial dysfunction. In addition, fars2-knockdown zebrafish presented pericardial edema and heart failure. FARS2 deficiency impaired mitochondrial homeostasis by directly blocking the aminoacylation of mt-tRNAPhe and inhibiting the synthesis of mitochondrial proteins, ultimately contributing to an imbalanced mitochondrial quality control system by accelerating mitochondrial hyperfragmentation and disrupting mitochondrion-related autophagy. Interfering with the mitochondrial quality control system using adeno-associated virus 9 or specific inhibitors mitigated the cardiac and mitochondrial dysfunction triggered by FARS2 deficiency by restoring mitochondrial homeostasis. CONCLUSIONS: Our findings unveil the previously unrecognized role of FARS2 in heart and mitochondrial homeostasis. This study may provide new insights into the molecular diagnosis and prevention of heritable cardiomyopathy as well as therapeutic options for FARS2-associated cardiomyopathy.

Examining the linkage between economic policy uncertainty, coal price, and carbon pricing in China: Evidence from pilot carbon markets.

Economic policies affect companies' production decisions. And the energy consumption volume is an intuitive reflection of the enterprise's production decisions. In China, coal is the main source of carbon emissions and the most important energy source. Therefore, the coal market and the uncertainty of economic policies are both directly tied to the carbon market. This study explores both the direct impact of economic policy uncertainty and coal price on carbon prices as well as the indirect impact of economic policy uncertainty on carbon prices through coal prices by utilizing the DCC-GARCH model and the NARDL model. The findings indicate that the dynamic correlations between coal prices and the CEPU are always negative and that those between the price of carbon and the CEPU vary by area. Meanwhile, the dynamic correlations between coal and carbon prices are only positive in Shenzhen and Beijing. Both coal prices and economic policy uncertainty produce asymmetrical impacts on carbon prices. Some policy implications are provided for developing the carbon markets in light of the results drawn from the study.

Weakly supervised learning for pattern classification in serial femtosecond crystallography.

Serial femtosecond crystallography at X-ray free electron laser facilities opens a new era for the determination of crystal structure. However, the data processing of those experiments is facing unprecedented challenge, because the total number of diffraction patterns needed to determinate a high-resolution structure is huge. Machine learning methods are very likely to play important roles in dealing with such a large volume of data. Convolutional neural networks have made a great success in the field of pattern classification, however, training of the networks need very large datasets with labels. This heavy dependence on labeled datasets will seriously restrict the application of networks, because it is very costly to annotate a large number of diffraction patterns. In this article we present our job on the classification of diffraction pattern by weakly supervised algorithms, with the aim of reducing as much as possible the size of the labeled dataset required for training. Our result shows that weakly supervised methods can significantly reduce the need for the number of labeled patterns while achieving comparable accuracy to fully supervised methods.

The relationship between entrepreneurial energy efficiency orientation and carbon footprint reduction: The mediating role of green networking and identification of barriers to green practices.

Motivated by the growing attention to climate change and the crucial role businesses could play in reducing greenhouse gas emissions, this study investigates entrepreneurial energy efficiency orientation in the context of carbon footprint reduction initiatives of small-and medium-sized enterprises (SMEs). We enhance understanding of the climate change action of SMEs by taking into account the mediating mechanisms (i.e., identification of green barriers and green networking) through which firm entrepreneurial energy efficiency orientation leads to superior carbon footprint reduction initiatives by overcoming barriers to green practices. A survey of 252 SME owners and top managers in the Tees Valley region, Northeast England, supported the direct impact of entrepreneurial energy efficiency orientation on overcoming barriers to green practices and the mediating role of identification of green barriers and green networking in this focal relationship. These findings reveal the importance of entrepreneurial energy efficiency orientation, identification of green barriers and green networking in helping SMEs overcome barriers to green practices and improving carbon footprint reduction initiatives.

The Critical Role of The Piezo1/ß-catenin/ATF4 Axis on The Stemness of Gli1+ BMSCs During Simulated Microgravity-Induced Bone Loss.

Disuse osteoporosis is characterized by decreased bone mass caused by abnormal mechanical stimulation of bone. Piezo1 is a major mechanosensitive ion channel in bone homeostasis. However, whether intervening in the action of Piezo1 can rescue disuse osteoporosis remains unresolved. In this study, a commonly-used hindlimb-unloading model is employed to simulate microgravity. By single-cell RNA sequencing, bone marrow-derived mesenchymal stem cells (BMSCs) are the most downregulated cell cluster, and coincidentally, Piezo1 expression is mostly enriched in those cells, and is substantially downregulated by unloading. Importantly, activation of Piezo1 by systemically-introducing yoda1 mimics the effects of mechanical stimulation and thus ameliorates bone loss under simulated microgravity. Mechanistically, Piezo1 activation promotes the proliferation and osteogenic differentiation of Gli1+ BMSCs by activating the ß-catenin and its target gene activating transcription factor 4 (ATF4). Inhibiting ß-catenin expression substantially attenuates the effect of yoda1 on bone loss, possibly due to inhibited proliferation and osteogenic differentiation capability of Gli1+ BMSCs mediated by ATF4. Lastly, Piezo1 activation also slightly alleviates the osteoporosis of OVX and aged mice. In conclusion, impaired function of Piezo1 in BMSCs leads to insufficient bone formation especially caused by abnormal mechanical stimuli, and is thus a potential therapeutic target for osteoporosis.

Non-invasive prenatal testing for fetal Ss, Kidd, and CTL2 blood group prediction by multiplex digital droplet PCR.

Background: Some blood groups, such as S and s blood groups in the MNS blood group system, and Kidd and CTL2 blood group systems, can cause severe fetal and newborn alloimmune disorders. Non-invasive prenatal testing (NIPT) to predict fetal blood groups and knowledge of local blood group gene frequency are both important for pregnancy management decisions. Droplet digital PCR (ddPCR) has high specificity and sensitivity in detecting fetal single nucleotide variation. Objectives: The objective is to predict fetal Ss, Kidd, and CTL2 blood groups using multiplex ddPCR. The gene frequencies of three blood groups were detected by ddPCR in northwest China. Design: This is a prospective study. Methods: Cell-free fetal DNA isolated from 26 healthy single pregnant women at different gestational stages was tested with QX200 Droplet Digital PCR. Results were compared with fetal genotypes. DNA samples purified from 20 blood pools containing a total of 1000 donors in northwest China were subjected to ddPCR to detect the gene frequency of three blood groups. Results: Ss, Kidd, and CTL2 blood groups of 26 pregnant fetuses were accurately detected by multiplex ddPCR. The multiplex ddPCR results were consistent with the Sanger sequencing results of 26 fetal blood samples after birth. The gene frequencies of the three blood groups detected by ddPCR were 9.30% for S, 90.70% for s, 48.43% for Jka, 51.57% for Jkb, 66.57% for HNA-3A, and 33.43% for HNA-3B. Conclusions: It is reliable to predict fetal Ss, Kidd, and CTL2 blood groups by multiplex ddPCR. Meanwhile, we designed a simple and efficient method for inferring the gene frequency of three blood groups based on ddPCR.

Poverty alleviation and rural revitalization: Perspective of fiscal spending and data evidence from 81 Chinese counties.

This paper builds a theoretical model of government performance functions for poverty alleviation using county-level panel data from 81 counties in China from 2014 to 2019. It uses a Panel-Tobit model and mechanism tests to verify the effect of fiscal policies on poverty reduction, and consolidates the robustness of the results through a series of extended methods, such as endogeneity treatment, robustness tests, and heterogeneity analysis. The results show that (1) poverty-related allocations can significantly reduce poverty incidence, and the effect of poverty reduction is more pronounced in poor counties; (2) public spending can significantly reduce poverty incidence, and the effect of poverty reduction through public spending is more pronounced in the sample of poor counties and nonfunded pilot counties; (3) poverty reduction can affect poverty incidence through primary and secondary industry development, and the effect of poverty reduction through primary industry development is more significant, while public spending does not affect poverty incidence through primary and secondary industries; and (4) improving health services can reduce poverty to a large extent, while education development has no effect on poverty reduction due to the long return cycle. This study suggests increasing the size of poverty-specific allocations and public spending, strengthening industry support, and implementing differentiated policy initiatives according to local conditions to improve the impact of poverty reduction.

Risk modelling of ESG (environmental, social, and governance), healthcare, and financial sectors.

Climate change poses enormous ecological, socio-economic, health, and financial challenges. A novel extreme value theory is employed in this study to model the risk to environmental, social, and governance (ESG), healthcare, and financial sectors and assess their downside risk, extreme systemic risk, and extreme spillover risk. We use a rich set of global daily data of exchange-traded funds (ETFs) from 1 July 1999 to 30 June 2022 in the case of healthcare and financial sectors and from 1 July 2007 to 30 June 2022 in the case of ESG sector. We find that the financial sector is the riskiest when we consider the tail index, tail quantile, and tail expected shortfall. However, the ESG sector exhibits the highest tail risk in the extreme environment when we consider a shock in the form of an ETF drop of 25% or 50%. The ESG sector poses the highest extreme systemic risk when a shock comes from China. Finally, we find that ESG and healthcare sectors have lower extreme spillover risk (contagion risk) compared to the financial sector. Our study seeks to provide valuable insights for developing sustainable economic, business, and financial strategies. To achieve this, we conduct a comprehensive risk assessment of the ESG, healthcare, and financial sectors, employing an innovative approach to risk modelling in response to ecological challenges.

Assessing the environmental impacts of renewable energy sources: A case study on air pollution and carbon emissions in China.

This study investigates the impact of renewable and non-renewable energy sources on carbon emissions in the context of China's 14th Five-Year Plan (2021-2025). The plan emphasises a "Dual-control" strategy of simultaneously setting energy consumption limits and reducing energy intensity for GDP (gross domestic product) in order to meet the targets of the five-year plan. Using a comprehensive dataset of Chinese energy and macroeconomic information spanning from 1990 to 2022, we conduct a Granger causality analysis to explore the relationship between energy sources and the level of air pollution. Our findings reveal a unidirectional link, wherein renewable energy contributes to a reduction in air pollution, while non-renewable energy sources lead to an increase. Despite the government's investment in renewable energy, our results show that China's economy remains heavily reliant on traditional energy sources (e.g., fossil fuels). This research is the first systematic examination of the interplay between energy usage and carbon emissions in the Chinese context. Our findings provide valuable insights for policy and market strategies aimed at promoting carbon neutrality and driving technological advancements in both government and industries.

Targeting a xenobiotic transporter to ameliorate vincristine-induced sensory neuropathy.

Vincristine is a widely used chemotherapeutic drug for the treatment of multiple malignant diseases that causes a dose-limiting peripheral neurotoxicity. There is no clinically effective preventative treatment for vincristine-induced sensory peripheral neurotoxicity (VIPN), and mechanistic details of this side effect remain poorly understood. We hypothesized that VIPN is dependent on transporter-mediated vincristine accumulation in dorsal root ganglion neurons. Using a xenobiotic transporter screen, we identified OATP1B3 as a neuronal transporter regulating the uptake of vincristine. In addition, genetic or pharmacological inhibition of the murine orthologue transporter OATP1B2 protected mice from various hallmarks of VIPN - including mechanical allodynia, thermal hyperalgesia, and changes in digital maximal action potential amplitudes and neuronal morphology - without negatively affecting plasma levels or antitumor effects of vincristine. Finally, we identified α-tocopherol from an untargeted metabolomics analysis as a circulating endogenous biomarker of neuronal OATP1B2 function, and it could serve as a companion diagnostic to guide dose selection of OATP1B-type transport modulators given in combination with vincristine to prevent VIPN. Collectively, our findings shed light on the fundamental basis of VIPN and provide a rationale for the clinical development of transporter inhibitors to prevent this debilitating side effect.

New practice for investors in Chinese stock market: From perspective of fractionally integrated realized GARCH model.

In this paper, based on the Realized GARCH model, the fractional integration Realized GARCH model is proposed by combining long memory parameters with conditional variance and replacing the original realized measure with the realized measure obtained after daily, weekly and monthly weighting. Based on the 5-min high-frequency data of the SSE index, the fractional integration Realized GARCH model, Realized HAR GARCH model and Realized GARCH model are investigated for their fitting effect and predictive ability on market volatility, and Monte Carlo simulations are conducted from the error terms obeying normal distribution, t-distribution and chi-square distribution so as to compare the RMSE and MAE of the three types of models with respect to conditional variance. The empirical results show that the fractionally integrated Realized GARCH model is found to better capture the long-run correlation in volatility in certain intervals by comparing the theoretical and sample auto-correlation functions, while the overall predictive power of the model is better than the other two models. Finally, it provides technical support and suggestions for investors' risk control.

Evodiamine inhibits ESCC by inducing M-phase cell-cycle arrest via CUL4A/p53/p21 axis and activating noxa-dependent intrinsic and DR4-dependent extrinsic apoptosis.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a malignancy with high incidence in several regions of China, and the prognosis of patients with ESCC is unfavorable. Evodiamine (Evo), a small molecule derived from the traditional Chinese herb Evodia rutaecarpa, has shown anti-cancer efficacy in numerous human malignancies but not in ESCC. PURPOSE: To determine whether Evo induces cell-cycle arrest and apoptosis in ESCC in vitro and in vivo and elucidate the underlying mechanisms. METHODS: ATPlite and colony formation assay were used to validate the inhibiting effect of Evo on three ESCC cells in vitro; Two subcutaneous tumor models of ESCC cells were used to evaluate the anti-ESCC effect of Evo and assess the biosafety of Evo in vivo; RNAseq and Database of KEGG pathway analysis provided a direction for the mechanistic study of Evo; FACS was used to detect Evo-induced cell cycle arrest and cell apoptosis in ESCC cells; Western blot and QPCR were respectively used to detect the level of related genes and proteins in Evo-treated ESCC cells; SiRNA and other experimental techniques were used to identify the molecular mechanism of Evo-induced ESCC cell cycle arrest and cell apoptosis. RESULTS: Evo significantly suppressed the growth of ESCC both in vitro and in vivo. Mechanistically, Evo induced M-phase cell-cycle arrest by inactivation of CUL4A E3 ligase, which mediates degradation blockage of p53 and transcriptional activation of p21. With the prolonged treatment time, Evo triggered both Noxa-dependent intrinsic and DR4-dependent extrinsic cell apoptosis in two ESCC cell lines. CONCLUSION: Our findings revealed the anti-tumor efficacy and mechanisms of Evo, providing a solid scientific basis for Evo as an attractive choice for ESCC treatment.

The causal relationship between green finance and geopolitical risk: Implications for environmental management.

This study investigates the time-varying causal relationship between geopolitical risk and green finance during the period of 1 March 2012-February 16, 2022. By using the novel time-varying causality testing framework, our findings shed light on the nexus between geopolitical risk and green finance in informing environmental management decisions. First, we find that time heterogeneity does exist in the causal relations between geopolitical risk and green finance. Second, geopolitical risk has a more prolonged impact on the volatility of green bonds and renewable energy than the return. Yet, geopolitical risk tends to influence the return of clean energy more persistently than volatility. Third, we observe that geopolitical risk has a more sustained impact on the return and volatility of renewable energy than clean energy. This might be due to the distinct nature of the production of clean energy and renewable energy, thereby providing implications for effective environmental management. Lastly, this paper demonstrates that the impact of geopolitical risk on the return of European clean energy has diminished since the onset of 2015. The volatility of the European clean energy sector is not affected by global geopolitical risk, underscoring the necessity of promoting the development of this sector to reduce the dependence on fossil fuels and enhance energy independence.

Power of Digital Economy to Drive Urban-Rural Integration: Intrinsic Mechanism and Spatial Effect, from Perspective of Multidimensional Integration.

The consensus that the digital economy drives urban-rural integration has been gradually reached both in practice and theory. Besides, the way by which the digital economy drives urban-rural integration remains updated iteratively. The coming period is an important opportunity to break down the dualistic urban-rural structure and improve the urban-rural integration development. It is also a critical stage for China to promote the deep integration of the digital economy and the real economy. In this study, the intrinsic mechanism of the digital economy in driving the four dimensions of urban-rural integration was elaborated. An analysis was made of the spatial effects in 30 provinces (municipalities and autonomous regions) of China during 2011-2019 using Bivariate Global Moran's I and geographically and temporally weighted regression (GTWR) models. As revealed by the results: (1) the digital economy and the four dimensions of urban-rural integration advance steadily, in which the convergence degree of urban and rural resident consumption is comparatively higher; (2) there is a significant spatial auto-correlation between the digital economy and the four dimensions of urban-rural integration, with the influence gradually strengthened with time; (3) the digital economy exerts mainly positive impacts on the equivalent allocation of urban and rural factors, integration of three industries in urban and rural areas, and convergence degree of urban and rural resident consumption, but inhibits the equalization of urban and rural public services in nearly half research areas; (4) both digital equipment basis and user basis play a vital role in promoting the four dimensions of urban-rural integration.

Aberrant activation of TGF-ß1 induces high bone turnover via Rho GTPases-mediated cytoskeletal remodeling in Camurati-Engelmann disease.

In the adult skeleton, the bone remodeling process involves a dynamic coordination between osteoblasts and osteoclasts, which is disrupted in diseases with high bone turnover rates and dysregulated transforming growth factor beta 1 (TGF-ß1). However, little is known about how TGF-ß1 signaling mediates bone resorption. Here, we described a pedigree with a heterozygous variant in TGF-ß1 (R218C) that resulted in aberrant activation of TGF-ß1 through an activating mechanism that caused Camurati-Engelmann disease (CED). We showed that CED patients have high levels of active Rho GTPases and the migration-related proteins Integrin ß1 and Integrin ß3 in their peripheral blood. HEK293T cells transfected with a plasmid encoding this mutant expressed high levels of TGF-ß1 and active Rho GTPases. Furthermore, activation of Rho by TGF-ß1 increased osteoclast formation and bone resorption, with increased migration of pre-osteoclasts, as well as cytoskeletal remodeling of pre-osteoclasts and mature osteoclasts. Importantly, pharmacological inhibition of Rho GTPases effectively rescued hyperactive TGF-ß1-induced osteoclastogenesis in vitro. Overall, we propose that Rho GTPases mediate TGF-ß1-induced osteoclastogenesis and suggest that Rho-TGF-ß1 crosstalk is associated with high bone turnover in CED.

Effect of Hip Joint Center on Multi-body Dynamics and Contact Mechanics of Hip Arthroplasty for Crowe IV Dysplasia.

OBJECTIVE: To investigate the hip joint forces, Von Mises stress, contact pressure and micro-motion of hip prosthesis for developmental dysplasia of the hip (DDH) patients under different hip joint centers using musculoskeletal (MSK) multi-body dynamics and finite element analysis. METHODS: Both MSK multi-body dynamics model and finite element (FE) model were based on CT data of a young female DDH patient with total hip replacement and were developed to study the biomechanics of the S-ROM hip prosthesis. The same offset of hip joint center along all six orientations compared with the standard position was set to predict its effects on both MSK multi-body dynamics and contact mechanics during one gait cycle. RESULTS: The hip joint forces in the entire walking gait cycle showed two peak values and clear differences between them under different hip joint centers. The hip joint force increased when the hip joint center moved posteriorly (2101 N) and laterally (1969 N) to the anatomical center (1848 N) at the first peak by 13.7% and 6.6%, respectively. The hip joint force increased sharply when the hip center deviated laterally (2115 N) and anteriorly (2407 N), respectively, from the standard position (1742 N) at the second peak. For the sleeve of the S-ROM prosthesis, the maximum Von Mises stress and contact pressure of the sleeve increased if the hip joint center deviated from the anatomical center posteriorly at the first peak. However, the Von Mises stresses and contact pressure increased at anterior and lateral orientations, compared to that of the standard position at the second peak. Small changes were observed for the maximum relative sliding distance along most of the orientations at both peaks except in the lateral and medial orientations, in which an increase of 8.6% and a decrease of 13.6% were observed, respectively. CONCLUSION: The hip joint center obviously influenced the hip joint forces, stress, contact pressure and micro-motion of the hip implant for this female patient.

TFG mutation induces haploinsufficiency and drives axonal Charcot-Marie-Tooth disease by causing neurite degeneration.

AIMS: TFG-related axonal Charcot-Marie-Tooth (CMT) disease is a late-onset, autosomal dominant, hereditary motor, and sensory neuropathy characterized by slowly progressive weakness and atrophy of the distal muscles. The objective of this study was to determine the common pathogenic mechanism of TFG-related CMT type 2 (CMT2) caused by different mutations and establish a direct association between TFG haploinsufficiency and neurodegeneration. METHODS: Three individuals carrying the TFG p.G269V mutation but with varying disease durations were studied. The effect of the p.G269V mutation was confirmed by analyzing protein samples extracted from the blood of two individuals. The functional consequences of both CMT2 mutant gene products were evaluated in vitro. The effect of TFG deficiency in the nervous system was examined using zebrafish models and cultured mouse neurons. RESULTS: Overexpression of p.G269V TFG failed to enhance soluble TFG levels by generating insoluble TFG aggregates. TFG deficiency disrupted neurite outgrowth and induced neuronal apoptosis both in vivo and in vitro and further impaired locomotor capacity in zebrafish, which was consistent with the phenotype in patients. Wnt signaling was activated as a protective factor in response to TFG deficiency. CONCLUSION: CMT2-related TFG mutation induces TFG haploinsufficiency within cells and drives disease by causing progressive neurite degeneration.

A multilingual dataset of COVID-19 vaccination attitudes on Twitter.

Vaccine hesitancy is considered as one main cause of the stagnant uptake ratio of COVID-19 vaccines in Europe and the US where vaccines are sufficiently supplied. A fast and accurate grasp of public attitudes toward vaccination is critical to addressing vaccine hesitancy, and social media platforms have proved to be an effective source of public opinions. In this paper, we describe the collection and release of a dataset of tweets related to COVID-19 vaccines. This dataset consists of the IDs of 2,198,090 tweets collected from Western Europe, 17,934 of which are annotated with the originators' vaccination stances. Our annotation will facilitate using and developing data-driven models to extract vaccination attitudes from social media posts and thus further confirm the power of social media in public health surveillance. To lay the groundwork for future research, we not only perform statistical analysis and visualization of our dataset, but also evaluate and compare the performance of established text-based benchmarks in vaccination stance extraction. We demonstrate one potential use of our data in practice in tracking the temporal changes in public COVID-19 vaccination attitudes.

Daurisoline Inhibits ESCC by Inducing G1 Cell Cycle Arrest and Activating ER Stress to Trigger Noxa-Dependent Intrinsic and CHOP-DR5-Dependent Extrinsic Apoptosis via p-eIF2α-ATF4 Axis.

Esophageal squamous cell carcinoma (ESCC), one of the most malignant human cancers in clinic, requires novel treatment. Daurisoline (DAS) is a component of traditional Chinese herb, which exhibits anti-cancer effects by autophagy inhibition and metastasis suppression. However, the effect and mechanism of DAS on ESCC remain unclear. Here, we found that DAS inhibited cell proliferation and colony formation in both human ESCC cell lines EC1 and ECA109. Mechanistically, DAS induced p21-/p27-dependent G1 phase cell cycle arrest and apoptosis in a dose-dependent manner. The induction of apoptosis by DAS was largely dependent on the activation of the transcription factor ATF4 and its downstream NOXA-dependent intrinsic and CHOP-DR5-dependent extrinsic apoptotic pathway. ATF4 activation induced by DAS was due to the generation of excessive reactive oxygen species (ROS) and the subsequent activation of endoplasmic reticulum (ER) stress through the p-eIF2α-ATF4 signal pathway, which can be largely abrogated by N-acetylcysteine (NAC), a scavenger of ROS. Moreover, DAS treatment significantly inhibited tumor growth and reduced tumor weight in the tumor xenograft mouse model by up-regulating key proteins related to cell cycle arrest and apoptotic pathway. Taken together, these findings identified DAS as a novel candidate for the treatment of ESCC.