Changes in the labor share of enterprises under the green and low-carbon transition: Empirical evidence from China's carbon emission trading pilot.

Green and low carbon reflect the high-quality development, while income distribution is an indicator of the balance of development. Is there a lack of fairness in the process of green and low carbon transition of enterprises? Using data from A-share listed companies from 2009 to 2016, this paper constructs a DID identification framework for controlling the endogeneity problem using the 2013 carbon trading policy pilot as a quasi-natural experiment to empirically test the impact of corporate low-carbon transformation on corporate labor income share in the context of carbon trading policy. The findings indicate that carbon trading policy decreases the labor income share of firms. In addition, we demonstrate that the low-carbon transition promotes labor productivity, suggesting that the Porter's hypothesis is confirmed in China, but the increase in labor wages is not in tandem with productivity growth, resulting in reduced labor income share. Heterogeneity analysis shows that the impact of carbon trading policy on labor income share is mainly pronounced in larger firms, high technology firms and persistent incumbent firms. Collectively, these results are expected to accurately improve our understanding on the impact of low-carbon transformation of enterprises on income distribution and provide reference for the government to formulate industrial policies and distribution mechanisms under low-carbon economy.

Masked Modeling-Based Ultrasound Image Classification via Self-Supervised Learning.

Recently, deep learning-based methods have emerged as the preferred approach for ultrasound data analysis. However, these methods often require large-scale annotated datasets for training deep models, which are not readily available in practical scenarios. Additionally, the presence of speckle noise and other imaging artifacts can introduce numerous hard examples for ultrasound data classification. In this paper, drawing inspiration from self-supervised learning techniques, we present a pre-training method based on mask modeling specifically designed for ultrasound data. Our study investigates three different mask modeling strategies: random masking, vertical masking, and horizontal masking. By employing these strategies, our pre-training approach aims to predict the masked portion of the ultrasound images. Notably, our method does not rely on externally labeled data, allowing us to extract representative features without the need for human annotation. Consequently, we can leverage unlabeled datasets for pre-training. Furthermore, to address the challenges posed by hard samples in ultrasound data, we propose a novel hard sample mining strategy. To evaluate the effectiveness of our proposed method, we conduct experiments on two datasets. The experimental results demonstrate that our approach outperforms other state-of-the-art methods in ultrasound image classification. This indicates the superiority of our pre-training method and its ability to extract discriminative features from ultrasound data, even in the presence of hard examples.

Exploring the Knowledge of An Outstanding Protein to Protein Interaction Transformer.

Protein-to-protein interaction (PPI) prediction aims to predict whether two given proteins interact or not. Compared with traditional experimental methods of high cost and low efficiency, the current deep learning based approach makes it possible to discover massive potential PPIs from large-scale databases. However, deep PPI prediction models perform poorly on unseen species, as their proteins are not in the training set. Targetting on this issue, the paper first proposes PPITrans, a Transformer based PPI prediction model that exploits a language model pre-trained on proteins to conduct binary PPI prediction. To validate the effectiveness on unseen species, PPITrans is trained with Human PPIs and tested on PPIs of other species. Experimental results show that PPITrans significantly outperforms the previous state-of-the-art on various metrics, especially on PPIs of unseen species. For example, the AUPR improves 0.339 absolutely on Fly PPIs. Aiming to explore the knowledge learned by PPITrans from PPI data, this paper also designs a series of probes belonging to three categories. Their results reveal several interesting findings, like that although PPITrans cannot capture the spatial structure of proteins, it can obtain knowledge of PPI type and binding affinity, learning more than binary PPI.

Poly(2-vinylpyridine)/MCM-41 Composites with Micropores and Switchable Mesopores for the Removal of Cr(VI).

Porous structure design and reversible regulation of pore size during adsorption-desorption are crucial to the removal of pollutants in water such as Cr(VI). In this paper, micropores and switchable mesopores were constructed on MCM-41 to further improve adsorption-desorption performance of Cr(VI) via the confinement effect of micropores and opening and closing of mesopores. 2-Vinylpyridine was introduced and polymerized into the pores and on the pore mouth of MCM41 modified by C═C group (AM41) under the irradiation of ultraviolet light. The obtained samples (PM41) possessed mesopores (2.73 nm) and micropores (1.36 nm), where mesopores could open or close under different pH and micropores showed the confinement effect because their pore size is close to Cr(VI) diameter (0.87 nm). Compared with MCM-41, the introduction of poly(2-vinylpyridine) enhanced obviously its adsorptive ability and it trapped most of the Cr(VI) (99%) in solution, 12 times higher than that of the parent sample. The change of pore size is favorable to the cycle performance, and after 3 times recycling, the removal rate of Cr(VI) by PM41-20 remained above 88%. Langmuir isotherm showed a better data correlation than the Freundlich model. Cr(VI) in solution was removed by electrostatic interaction between the pyridine group and Cr(VI) and the confinement effect from micropores.

Surgical repair of post myocardial infarction ventricular septal defect: a retrospective analysis of a single institution experience.

INTRODUCTION: Ventricular septal defect (VSD) is a mechanical complication of acute myocardial infarction (MI) with a very high mortality, despite advances in surgical and circulatory support. The tremendous hemodynamic disturbance and the severely fragile myocardium render surgical repair a great challenge. The optimal time of surgical repair with or without circulatory support is still controversial. OBJECTIVE: The aim of this study is to review our experience with early surgical repair of post-MI VSD in a single major cardiac institution in China. METHODS: From January 2013 to October 2020, 9consecutive patients presented to our emergency department with a diagnosis of post-MI VSD. Among them, 8 were male, and the mean age was 58 ± 7years. The mean VSD size was 22.5 ± 5.7 mm. In all patients, an intra-aortic balloon pump (IABP)was inserted immediately after admission to cardiac surgery service. All patients were operated at a mean of 3.3 ± 2.9 days, and 4 within 24 h of the rupture (range 1 to 9 days post-VSD). In 5 cases, the VSD was located superiorly, and 4 cases in the posterior septum. RESULTS: The overall 30-day mortality was 11% (1/9). Coronary angiography was performed in all nine patients, four with single vessel disease had coronary stents implanted, and the other five received concomitant coronary artery bypass grafting during VSD repair surgery. There was no death in all 5 patients with anterior septal perforation. One patient with posterior septal perforation died in the operating room due to bleeding from the ventriculotomy site. Three survived patients were diagnosed with a small residual defect and mild left to right shunt post-repair. However, no further intervention was required, and patients remained asymptomatic (Killip II in 1 and III in 2). CONCLUSION: In our experience, immediate insertion of IABP and hemodynamic stabilization with early surgical intervention of VSD repair and concomitant coronary revascularization provided an 89% survival rate.

Effects of oyster shells on maturity and calcium activation in organic solid waste compost.

With the rapid development of aquaculture, the production of oyster shells has surged, posing a potential threat to the environment. While oyster shell powder is widely recognized for its inherent alkalinity and rich calcium carbonate content, making it a superior soil conditioner, its role in organic solid waste composting remains underexplored. To investigate the effects of varying concentrations of oyster shell powder on compost maturation and calcium activation, this study employed thermophilic co-composting with acidic sugar residue and bean pulp, incorporating 0% (control), 10% (T1), 20% (T2), 30% (T3), and 40% (T4) oyster shell powder. Findings revealed that appropriate proportions of oyster shell powder significantly enhance temperature stability during composting and elevate maturation levels, notably reducing ammonia emissions between 62.5% and 76.7%. Intriguingly, the calcium in the oyster shell powder was significantly activated during composting, with the 40% addition group achieving the highest calcium activation rate of 48.5%. In summation, the inclusion of oyster shell powder not only optimizes the composting process but also efficiently activates the calcium, resulting in an alkaline organic-inorganic composite soil conditioner with high exchangeable calcium content. This research holds significant implications for promoting the high-value utilization of oyster shells.

The impact of household wealth gap on individual's mental health.

BACKGROUND: Improving the individual's mental health is important for sustainable economic and social development. Although some studies found that household wealth gap may affect individuals' mental health, few studies have clarified the causal relationship between household wealth gap between mental health in China. This study examines the impact of the household wealth gap on individuals' mental health using data from the 2012-2018 China Family Panel Survey. METHODS: This study first used the two-way fixed effects model to investigate the impact of household wealth gap on individuals' mental health. Considering the endogeneity, the two-stage least square and propensity score matching were employed to examine the impact of household wealth inequality on individuals' mental health. RESULTS: The results show that the household wealth gap has negative impact on individuals' mental health. A series of robustness tests support this conclusion. The results of heterogeneity analysis show that the impact of household wealth gap on mental health is more pronounced among middle-aged and elderly individuals, residents with lower education levels, and rural residents. The results of the mechanism analysis suggest that the household wealth gap may affect individuals' mental health by influencing the individual's health insurance investment and neighborhood relations. In addition, the household wealth gap not only significantly negatively affects individuals' mental health in the short term but also in the medium- to long-term. CONCLUSION: These findings suggest that the government should take various measures to narrow the wealth inequality between families, which may effectively improve the mental health of residents.

Self-supervised learning-based underwater acoustical signal classification via mask modeling.

The classification of underwater acoustic signals has garnered a great deal of attention in recent years due to its potential applications in military and civilian contexts. While deep neural networks have emerged as the preferred method for this task, the representation of the signals plays a crucial role in determining the performance of the classification. However, the representation of underwater acoustic signals remains an under-explored area. In addition, the annotation of large-scale datasets for the training of deep networks is a challenging and expensive task. To tackle these challenges, we propose a novel self-supervised representation learning method for underwater acoustic signal classification. Our approach consists of two stages: a pretext learning stage using unlabeled data and a downstream fine-tuning stage using a small amount of labeled data. The pretext learning stage involves randomly masking the log Mel spectrogram and reconstructing the masked part using the Swin Transformer architecture. This allows us to learn a general representation of the acoustic signal. Our method achieves a classification accuracy of 80.22% on the DeepShip dataset, outperforming or matching previous competitive methods. Furthermore, our classification method demonstrates good performance in low signal-to-noise ratio or few-shot settings.

Can government-led civilized city construction promote green innovation? Evidence from China.

Since the reform and opening up, China's urbanization level has rapidly increased. However, urban civilization construction lags behind, creating a series of problems, such as environmental pollution, further restricting the healthy and sustainable development of cities. Therefore, China has carried out top-down civilized city construction led by the government. This paper regards the "national civilized city" selection implemented by the Chinese government as a quasi-natural experiment and empirically tests the impact of government-led civilized city construction on urban green innovation and the mechanism of this impact by using the progressive difference-in-differences (DID) model based on panel data of 281 Chinese cities from 2003 to 2018. The results show that civilized city construction contributes to increasing the level of urban green innovation, which is supported by a series of robustness tests conducted through variable substitution and sample adjustment. The quantile regression results show that the higher the level of green innovation, the stronger the green innovation effect of civilized city construction. This finding indicates that the marginal effect of green innovation in civilized city construction is gradually enhanced with the gradual increase in the level of green innovation in cities. The mediating effect analysis shows that civilized city construction can provide financial support for green innovation activities by promoting the expansion of the urban investment scale, which, in turn, increases the level of urban green innovation. In addition, environmental regulation can strengthen the promoting effect of civilized city construction on green innovation. These findings hold important theoretical and practical significance for understanding the green innovation effect of government-led civilized city construction and further leveraging the important role of government in the construction of the urban business environment as a means to promote urban investment clustering, lay the factor foundation for green innovation and strengthen the synergy between environmental regulation policies and civilized city construction policies.

Household Wealth and Individuals' Mental Health: Evidence from the 2012-2018 China Family Panel Survey.

Based on the data from the 2012-2018 China Family Panel Survey, this study examines the impact of household wealth on individuals' mental health using a two-way fixed effects model. The findings indicate that household wealth exerts a significant positive effect on individuals' mental health. Furthermore, this study shows that the impact of household wealth on individuals' mental health is nonlinear but inverted U-shaped. Considering the possible endogeneity problem, this study further examines the effect of household wealth on residents' mental health using two-stage least squares, and the conclusions remain robust. The results of the heterogeneity analysis indicate that household wealth has a greater impact on the mental health of residents in the low-education group and western region. Furthermore, the results of the mechanisms reveal that household wealth affects mental health by influencing insurance investment and individuals' labor supply. Moreover, this study finds that household wealth affects individuals' mental health not only in the short term but also in the medium and long terms. This study provides policy implications for the government toward improving individuals' mental health.

The effect of venture capital on green innovation: Is environmental regulation an institutional guarantee?

Venture capital (VC) may play a role in urban green innovation (GI) by providing long-term financial support. Based on panel data from 150 cities in China, this study analyzes the impact of VC on urban GI and the underlying mechanism. The research conclusions are as follows. VC significantly promotes urban GI, and we find micro-level evidence for this conclusion. The results of a quantile regression show that with an improvement in a city's GI level, the positive effect of VC shows an increasing trend. A mechanism analysis shows that VC promotes urban GI by enhancing urban investment and innovative talent agglomeration and that the accumulation of high-quality human capital brought by VC is the main reason for its positive impact on urban GI. In addition, the influence of VC on urban GI exhibits a threshold effect based on environmental regulations. There is an optimal range of environmental regulation intensity that maximizes the effect of VC on GI. This study confirms the important role of VC in regional GI activities, enriching the research on the innovation effect of VC and providing a theoretical and practical reference for promoting green economic development.

Operando Elucidation of Electrocatalytic and Redox Mechanisms on a 2D Metal Organic Framework Catalyst for Efficient Electrosynthesis of Hydrogen Peroxide in Neutral Media.

The practical electrosynthesis of hydrogen peroxide (H2O2) is hindered by the lack of inexpensive and efficient catalysts for the two-electron oxygen reduction reaction (2e- ORR) in neutral electrolytes. Here, we show that Ni3HAB2 (HAB = hexaaminobenzene), a two-dimensional metal organic framework (MOF), is a selective and active 2e- ORR catalyst in buffered neutral electrolytes with a linker-based redox feature that dynamically affects the ORR behaviors. Rotating ring-disk electrode measurements reveal that Ni3HAB2 has high selectivity for 2e- ORR (>80% at 0.6 V vs RHE) but lower Faradaic efficiency due to this linker redox process. Operando X-ray absorption spectroscopy measurements reveal that under argon gas the charging of the organic linkers causes a dynamic Ni oxidation state, but in O2-saturated conditions, the electronic and physical structures of Ni3HAB2 change little and oxygen-containing species strongly adsorb at potentials more cathodic than the reduction potential of the organic linker (Eredox ∼ 0.3 V vs RHE). We hypothesize that a primary 2e- ORR mechanism occurs directly on the organic linkers (rather than the Ni) when E > Eredox, but when E < Eredox, H2O2 production can also occur through Ni-mediated linker discharge. By operating the bulk electrosynthesis at a low overpotential (0.4 V vs RHE), up to 662 ppm of H2O2 can be produced in a buffered neutral solution in an H-cell due to minimized strong adsorption of oxygenates. This work demonstrates the potential of conductive MOF catalysts for 2e- ORR and the importance of understanding catalytic active sites under electrochemical operation.

A Scaffold-based Deep Generative Model Considering Molecular Stereochemical Information.

Designing molecules with specific scaffolds can facilitate the discovery and optimization of lead compounds. Some scaffold-based molecular generation models have been developed using deep-learning methods based on specific scaffolds, although incorporating scaffold generalization is expected to achieve scaffold hopping. Moreover, most of the existing models focus on the 2D shape of the scaffold and overlook the stereochemical properties of the compound, especially for natural products. In this study, we optimized the scaffold-based molecular generation model designed by Lim et al. (Chemical Science 2020, 11, 1153-1164). Real-time ultrafast shape recognition with pharmacophore constraints (USRCAT) was introduced into the model to search for molecules similar to the 3D conformation and pharmacophore of the input scaffold sourced from the training set; the searched molecules were then used as new scaffolds to execute scaffold hopping. The optimized model could generate new molecules with the same chirality as the input scaffold. Furthermore, the probability distribution of the molecular structure and various physicochemical properties were analyzed to evaluate the model's generation capability. We thus believe that the optimized model can provide a basis for medicinal chemists to explore a wider chemical space toward optimization of the lead compounds and to screen the virtual compound library.

The impact of local government debt on urban environmental pollution and its mechanism: Evidence from China.

As an important financial means for governments to improve the quality of economic development, government debt greatly affects the quality of local environmental governance. Based on a theoretical mechanism analysis that uses the pollutant emissions panel data and new caliber urban investment bond data of 273 cities in China, this paper empirically tests the impact of local government debt on urban emission reduction and the mechanism that drives this impact. We find that local government debt significantly promotes urban emissions reduction, and as urban pollution becomes more aggravated, this promoting effect has a dynamic path, first strengthening and then weakening. The role of local government debt in promoting urban emission reduction is characterized by both temporal and spatial heterogeneity. A mechanistic analysis shows that local government debt can promote urban emission reduction by promoting urban environmental innovation, with green invention patents demonstrating a stronger intermediary role than green utility model patents.

Crowding in or crowding out? How local government debt influences corporate innovation for China.

The pressure upon local governments to redeem their debt could affect government fiscal ability. It could consequently affect their fiscal policies on corporations, which might distort corporate innovation. Based on the data of Chinese Shanghai and Shenzhen A-share listed companies and the local government implicit short-term debt financed by local government financing vehicles (LGFVs) in 31 provinces, this paper shows that local government debt (LGD) negatively affects corporate R&D investment in China, thereby suggesting a strong crowding-out effect. The crowding-out effect is more pronounced when the firm is a non-state-owned enterprise (NSOE), the firm's size is small, the firm's age is young, or the firm is in the lower market competition. This paper provide evidence by interacting the terms that local government actions, such as consumption of fiscal resources, strengthening tax collection efforts, or consumption of credit resources, might partially account for the crowding-out effect. This study illustrates the innovation costs of local government debt.

Relationships between migration and the fiscal sustainability of the pension system in China.

There are a few existing studies on whether domestic migration improves China's pension system's fiscal sustainability in the context of rapid urbanization and industrialization. In this paper, we systematically investigate the impact of migration on the solvency of the worker's old-age insurance for urban employees by constructing actuarial and econometric models. We use panel data from 2002 to 2018, collected from 31 provinces in China. The results show that the association between migration and the solvency of pensions is an inverted-U shape along the urbanization process. Further regional comparison showed that the above-stated inverted-U curve is more pronounced in the central and western regions. We also established that the number of participants and the contribution base are the main contributors to these results. Our conclusions are important for future population policies and public pension systems in China.

Long Noncoding RNA HCG18 Promotes Malignant Phenotypes of Breast Cancer Cells via the HCG18/miR-103a-3p/UBE2O/mTORC1/HIF-1α-Positive Feedback Loop.

In recent years, an increasing number of studies have reported that long noncoding RNAs (lncRNAs) play crucial roles in breast cancer (BC) progression and metastasis. Another study group of our research center reported that lncRNA HCG18 was one of the 30 upregulated lncRNAs in BC tissues compared with normal tissues in The Cancer Genome Atlas database. However, the exact biological roles of HCG18 in BC remain unclear. In this study, we demonstrated that HCG18 is significantly upregulated in BC tissues and cells and that BC patients with high HCG18 expression tend to have poor prognosis. In vitro assays indicated that HCG18 promotes BC cell proliferation and invasion and endows BC cells with cancer stemness properties. In vivo assays revealed that reducing HCG18 expression in the BC cell line MDA-MB-231 markedly decreased tumor growth and lung metastasis in xenograft mouse models. In terms of mechanism, we found that HCG18 positively regulated the expression of BC-related ubiquitin-conjugating enzyme E2O (UBE2O) by sponging miR-103a-3p, and our previous research verified that UBE2O could promote the malignant phenotypes of BC cells through the UBE2O/AMPKα2/mTORC1 axis. Furthermore, as a downstream target of the HCG18/miR-103a-3p/UBE2O/mTORC1 axis, hypoxia-inducible factor 1α transcriptionally promoted HCG18 expression and then formed a positive feedback loop in BC. Taken together, these results confirm that HCG18 plays an oncogenic role in BC and might serve as a prognostic biomarker and a potential therapeutic target for BC treatment.

Understanding the Mechanism of High Capacitance in Nickel Hexaaminobenzene-Based Conductive Metal-Organic Frameworks in Aqueous Electrolytes.

Recently, intrinsically conductive metal-organic frameworks (MOFs) have demonstrated promising performance in fast-charging energy storage applications and may outperform some current electrode materials (e.g., porous carbons) for supercapacitors in terms of both gravimetric and volumetric capacitance. In this report, we examine the mechanism of high capacitance in a nickel hexaaminobenzene-based MOF (NiHAB). Using a combination of in situ Raman and X-ray absorption spectroscopies, as well as detailed electrochemical studies in a series of aqueous electrolytes, we demonstrate that the charge storage mechanism is, in fact, a pH-dependent surface pseudocapacitance, and unlike typical inorganic systems, where transition metals change oxidation state during charge/discharge cycles, NiHAB redox activity is ligand-centered.

A Porphyrinic Zirconium Metal-Organic Framework for Oxygen Reduction Reaction: Tailoring the Spacing between Active-Sites through Chain-Based Inorganic Building Units.

The oxygen reduction reaction (ORR) is central in carbon-neutral energy devices. While platinum group materials have shown high activities for ORR, their practical uses are hampered by concerns over deactivation, slow kinetics, exorbitant cost, and scarce nature reserve. The low cost yet high tunability of metal-organic frameworks (MOFs) provide a unique platform for tailoring their characteristic properties as new electrocatalysts. Herein, we report a new concept of design and present stable Zr-chain-based MOFs as efficient electrocatalysts for ORR. The strategy is based on using Zr-chains to promote high chemical and redox stability and, more importantly, tailor the immobilization and packing of redox active-sites at a density that is ideal to improve the reaction kinetics. The obtained new electrocatalyst, PCN-226, thereby shows high ORR activity. We further demonstrate PCN-226 as a promising electrode material for practical applications in rechargeable Zn-air batteries, with a high peak power density of 133 mW cm-2. Being one of the very few electrocatalytic MOFs for ORR, this work provides a new concept by designing chain-based structures to enrich the diversity of efficient electrocatalysts and MOFs.

Effect of Antibiotics on the Microbial Efficiency of Anaerobic Digestion of Wastewater: A Review.

Recycling waste into new materials and energy is becoming a major challenge in the context of the future circular economy, calling for advanced methods of waste treatment. For instance, microbially-mediated anaerobic digestion is widely used for conversion of sewage sludge into biomethane, fertilizers and other products, yet the efficiency of microbial digestion is limited by the occurrence of antibiotics in sludges, originating from drug consumption for human and animal health. Here we present antibiotic levels in Chinese wastewater, then we review the effects of antibiotics on hydrolysis, acidogenesis and methanogenesis, with focus on macrolides, tetracyclines, ß-lactams and antibiotic mixtures. We detail effects of antibiotics on fermentative bacteria and methanogenic archaea. Most results display adverse effects of antibiotics on anaerobic digestion, yet some antibiotics promote hydrolysis, acidogenesis and methanogenesis.