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.

Climate change effects and their implications for the financial markets: Evidence from the United Kingdom.

This study aims to examine how the climate affects the behaviour of the stock market. To achieve this, we have drawn on daily data from Jan 2005 to Jan 31, 2023 and several environmental factors (e.g., temperature, humidity, cloud cover and visibility) to account for extreme weather conditions using the 21-day moving average and its standard deviation. The empirical analysis has revealed three key findings regarding the impact of weather on the stock market's behaviour. First, various forms of extreme weather conditions consistently lead to influence stock behaviour. Second, results provide valuable insights into market behaviour and help investors to make more informed investment decisions. Third, the weather conditions have new information about the climate risk and investors should react to it swiftly in light of our findings. The saliency theory can help reconcile the theoretical conflicts between the real options and risk-shifting theories when it comes to investing in uncertain and extreme climate conditions.

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.

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.

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.

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.

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.

Exploring Spillover Effects for COVID-19 Cascade Prediction.

An information outbreak occurs on social media along with the COVID-19 pandemic and leads to an infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its negative impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decisions to participate in diffusing certain information has not been studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures due to its special role in consolidating public efforts to slow down the spread of the virus. Through our collected Twitter dataset, we validate the existence of the spillover effects. Building on this finding, we propose extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effects significantly improves the state-of-the-art GNN methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 messages.

Endogenous Biomarkers for SLC Transporter-Mediated Drug-Drug Interaction Evaluation.

Membrane transporters play an important role in the absorption, distribution, metabolism, and excretion of xenobiotic substrates, as well as endogenous compounds. The evaluation of transporter-mediated drug-drug interactions (DDIs) is an important consideration during the drug development process and can guide the safe use of polypharmacy regimens in clinical practice. In recent years, several endogenous substrates of drug transporters have been identified as potential biomarkers for predicting changes in drug transport function and the potential for DDIs associated with drug candidates in early phases of drug development. These biomarker-driven investigations have been applied in both preclinical and clinical studies and proposed as a predictive strategy that can be supplanted in order to conduct prospective DDIs trials. Here we provide an overview of this rapidly emerging field, with particular emphasis on endogenous biomarkers recently proposed for clinically relevant uptake transporters.

Regio- and Stereoselective Synthesis of Highly Functionalized Tetrasubstituted Olefins by Iodine-Mediated Iodofunctionalization of Ferrocene-Containing Allenylphosphonates.

An iodine-mediated iodofunctionalization of ferrocene-containing allenylphosphonates with various carboxylic acids, alcohols, and phenols has been established, affording a series of highly functionalized tetrasubstituted alkenylphosphonate derivatives with high regio- and stereoselectivities in moderate to good yields. This approach provides an opportunity to the useful but not readily available tetrasubstituted olefins. The stereoselectivity of the reaction may be controlled by the neighboring group participation of the oxygen atom of the phosphate group.

[Detection and analysis of an ATP2A2 mutation in a family with Darier-White disease].

OBJECTIVE: To explore the molecular basis for a pedigree affected with Darier-White disease. METHODS: Genomic DNA was isolated from 3 patients and 1 unaffected member from the pedigree, as well as 80 healthy controls. Targeted sequence capture and next-generation sequencing were used to screen mutations of skin disease-related genes. Candidate mutations were verified by Sanger sequencing, and co-segregation analysis was carried out to confirm the pathogenicity of mutation. Conservation analysis and protein structure and function were also predicted with Bioinformatic tools. RESULTS: A heterozygous mutation c.2246G>T (p.G749V) was identified in exon 15 of ATP2A2 gene in all 3 patients from the pedigree, but not in the unaffected member or 80 healthy controls. The corresponding amino acid was highly conserved, and mutation of which can lead to structural and functional changes of the protein. CONCLUSION: The c.2246G>T missense mutation of the ATP2A2 gene probably underlies the Darier-White disease in this pedigree by causing damages to the structure and function of sarcoplasmic/endoplasmic reticulum calcium ATPase 2 (SERCA2).

Two novel CPS1 mutations in a case of carbamoyl phosphate synthetase 1 deficiency causing hyperammonemia and leukodystrophy.

BACKGROUND: Carbamoyl phosphate synthetase 1 deficiency (CPS1D) is a rare autosomal recessive disorder of the urea cycle, mostly characterized by hyperammonemia and the concomitant leukodystrophy. The onset of CPS1D can be at any age, and the clinical manifestations are variable and atypical. Genetic tests are indispensable for accurate diagnosis of CPS1D on the basis of biochemical tests. METHODS: Blood tandem mass spectrometric analysis and urea organic acidemia screening were performed on a Chinese neonatal patient with low activity, recurrent seizures, and hyperammonemia. Next-generation sequencing and Sanger sequencing were followed up for making a definite diagnosis. Bioinformatics tools were used for the conservation analysis and pathogenicity predictions of the identified mutations. RESULTS: Increased lactate in urea and decreased citrulline in blood were detected in the patient. Two novel mutations (c.173G>T, p.G58V in exon 2 and c.796G>A, p.G266R in exon 8) in CPS1 identified in the neonatal patient were found through coseparation verification. Both of the two mutations were predicted to be deleterious, and the two relevant amino acids exerted highly evolutionarily conserved. The final diagnosis of the patient was compound heterozygous CPS1D. CONCLUSION: This study described the specific clinical characteristics and the variations of physiological and biochemical indices in a Chinese neonatal patient with CPS1D, which facilitated the diagnosis and mechanism research of the disease. Two novel causative missense mutations were identified, which enriched the mutation spectrum of CPS1D in China and worldwide. Advice of prenatal diagnosis was given to the family for a new pregnancy.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN.

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

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.

Top of tide: Nexus between organization agility, digital capability and top management support in SME digital transformation.

In the swiftly evolving business landscape, digital transformation (DT) has emerged as a crucial strategy for firms to gain a competitive edge. Despite the abundance of literature on DT in firms, there remains a dearth of empirical research that defines and analyzes crucial antecedents of small and medium-sized enterprises' (SMEs) DT from an internal perspective. To fill this research gap, this study examines the correlation between organizational agility and digital capability in cultivating SMEs' DT while also evaluating top management support as a moderating variable through the lens of internal factors of SMEs. The results indicate that both organizational agility and digital capability have a positive impact on SMEs' DT, with organizational agility significantly influencing digital capability. Furthermore, the findings highlight that digital capability serves as a mediator between organizational agility and SMEs' DT. In addition, top management support plays a moderating role in these relationships to a certain extent. Additionally, we explicate the concept of digital capabilities from the perspective of dynamic capability. Our study contributes to an enhanced understanding of the effect of organizational agility and digital capability on SMEs' DT, as well as the role of top management support. We provide recommendations for managers to enhance organizational agility and suggest that SMEs should improve their digital thinking to better perceive digital technology changes, enhance digital operation capabilities, and better integrate digital resources.

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.

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.

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.