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The research on how environmental performance feedback (EPF) affect corporate environmental investment (EI) is in infancy. We divide EPF into positive and negative EPF and examine the effects on EI. We further study the moderating roles of environmental regulation. By using panel data of listed manufacturing companies in China, we find positive EPF negatively affects EI, whereas negative EPF positively affects EI. Mandatory environmental regulation strengthens the relationship between negative EPF and EI, while voluntary environmental regulation weakens the relationship between negative EPF and EI. Furthermore, we find the impact of EPF on EI is heterogeneous.

Marker-assisted selection (MAS) and genomic selection (GS) breeding have greatly improved the efficiency of rice breeding. Due to the influences of epistasis and gene pleiotropy, ensuring the actual breeding effect of MAS and GS is still a difficult challenge to overcome. In this study, 113 indica rice varieties (V) and their 565 testcross hybrids (TC) were used as the materials to investigate the genetic basis of 12 quality traits and nine agronomic traits. The original traits and general combining ability of the parents, as well as the original traits and mid-parent heterosis of TC, were subjected to genome-wide association analysis. In total, 381 primary significantly associated loci (SAL) and 1,759 secondary SALs that had epistatic interactions with these primary SALs were detected. Among these loci, 322 candidate genes located within or nearby the SALs were screened, 204 of which were cloned genes. A total of 39 MAS molecular modules that are beneficial for trait improvement were identified by pyramiding the superior haplotypes of candidate genes and desirable epistatic alleles of the secondary SALs. All the SALs were used to construct genetic networks, in which 91 pleiotropic loci were investigated. Additionally, we estimated the accuracy of genomic prediction in the parent V and TC by incorporating either no SALs, primary SALs, secondary SALs or epistatic effect SALs as covariates. Although the prediction accuracies of the four models were generally not significantly different in the TC dataset, the incorporation of primary SALs, secondary SALs, and epistatic effect SALs significantly improved the prediction accuracies of 5 (26%), 3 (16%), and 11 (58%) traits in the V dataset, respectively. These results suggested that SALs and epistatic effect SALs identified based on an additive genotype can provide considerable predictive power for the parental lines. They also provide insights into the genetic basis of complex traits and valuable information for molecular breeding in rice.

The impact of environmental, social, and governance (ESG) performance gaps on firm green innovation is examined in this paper by a panel database of A-share Chinese listed companies from 2011 to 2021. Using multiple linear regression and conducting a series of endogeneity tests and robustness checks, our empirical analysis shows that firm ESG performance gaps have significantly positive effect on green innovation. Both ESG performance below historical aspiration and social aspiration levels enhance a firm’s green innovation. Confucian culture negatively moderates the positive relationship between ESG performance gaps and green innovation, suggesting that firms more influenced by Confucian culture exhibit reduced green innovation than those less influenced. Additionally, firm digitalization positively moderates the positive relationship between ESG performance gaps and green innovation, indicating that firms with higher levels of digitalization are better equipped to improve green innovation when facing ESG performance shortfalls. This study extends the existing knowledge of firm ESG performance and motivation of green innovation. The research findings offer practical insights for leveraging the motivation and capabilities of green innovation to attain firm ESG objectives.

Currently, closed-set object detection models represented by YOLO are widely deployed in the industrial field. However, such closed-set models lack sufficient tuning ability for easily confused objects in complex detection scenarios. Open-set object detection models such as GroundingDINO expand the detection range to a certain extent, but they still have a gap in detection accuracy compared with closed-set detection models and cannot meet the requirements for high-precision detection in practical applications. In addition, existing detection technologies are also insufficient in interpretability, making it difficult to clearly show users the basis and process of judgment of detection results, causing users to have doubts about the trust and application of detection results. Based on the above deficiencies, we propose a new object detection algorithm based on multi-modal large language models that significantly improves the detection effect of closed-set object detection models for more difficult boundary tasks while ensuring detection accuracy, thereby achieving a semi-open set object detection algorithm. It has significant improvements in accuracy and interpretability under the verification of seven common traffic and safety production scenarios.

Background/Objectives: To investigate the crisping mechanism of broad bean-based crisping diets on Nile Tilapia. Methods: Four crisping diets were designed to feed 360 fish for 90 days, and multiomics analyses were employed. Results: Our results indicated that the designed crisping diets for Nile tilapia can effectively make tilapia muscle crispy. The ingestion of broad bean-based diets induced metabolic reprogramming dominated by glycolytic metabolism inhibition in fish, and metabolic reprogramming is the initiator of muscle structural remodeling. Among these, glucose is the main DAMP to be recognized by cellular PRRs, activating further immune response and oxidative stress and finally resulting in muscle change. Conclusions: Based on our results of multiomics, pck2, and ldh played main roles in crisping molecular mechanisms in driving the initial metabolic reprogram. Moreover, the addition of the crisping package further activated the ErbB signaling pathway and downstream MAPK signaling pathway to strengthen immune response, promoting muscle fiber development and growth. Our study delved into the effects of crisping formula diet on the liver of Nile tilapia at the molecular level, providing theoretical guidance for the nutritional regulation of crispy Nile tilapia.

Abstract Genetic improvement of grain quality is more challenging in hybrid rice than in inbred rice due to additional nonadditive effects such as dominance. Here, we describe a pipeline developed for joint analysis of phenotypes, effects, and generations (JPEG). As a demonstration, we analyze 12 grain quality traits of 113 inbred lines (male parents), five tester lines (female parents), and 565 (113×5) of their hybrids. We sequence the parents for single nucleotide polymorphisms calling and infer the genotypes of the hybrids. Genome-wide association studies with JPEG identify 128 loci associated with at least one of the 12 traits, including 44, 97, and 13 loci with additive effects, dominant effects, and both additive and dominant effects, respectively. These loci together explain more than 30% of the genetic variation in hybrid performance for each of the traits. The JEPG statistical pipeline can help to identify superior crosses for breeding rice hybrids with improved grain quality.

Triple-negative breast cancer (TNBC) stands out as the most aggressive subtype within the spectrum of breast cancer. The current clinical guidelines propose treatment strategies involving cytotoxic agents like epirubicin or paclitaxel. However, the emergence of acquired resistance frequently precipitates secondary tumor recurrence or the spread of metastasis. In recent times, significant attention has been directed toward the transcription factor RUNX2, due to its pivotal role in both tumorigenesis and the progression of cancer. Previous researches suggest that RUNX2 might be intricately linked to the development of resistance against chemotherapy, with its mechanism of action possibly intertwined with the signaling of TGF-β. Nevertheless, the precise interplay between their effects and the exact molecular mechanisms underpinning chemoresistance in TNBC remain elusive. Therefore, we have taken a multifaceted approach from in vitro and in vivo experiments to validate the relationship between RUNX2 and TGF-β and to search for their pathogenic mechanisms in chemoresistance. In conclusion, we found that RUNX2 affects chemoresistance by regulating cancer cell stemness through direct binding to TGF-β, and that TGF-β dually regulates RUNX2 expression. The important finding will provide a new reference for clinical reversal of the development of chemoresistance in breast cancer.

Abstract Accurate assessment of drought recovery probability is crucial for proactive strategies in cost‐effective water resource allocation, resilient agriculture management, and sustainable energy planning. However, there has been a notable gap in assessing drought recovery from a probabilistic and multi‐variate perspective. Here, we develop a Vine Copula‐based framework to quantify global drought recovery probability and explore its elasticity to precipitation changes. Compared to the historical period (1951–1983), 51% of global land has become increasingly difficult to recover from extreme droughts over 1984–2016 within 8–14 days during growing seasons. Furthermore, the response of global drought recovery to precipitation changes depends on the background climate and varies asymmetrically between wet and dry conditions. Under an extremely wet climate, a 1% historical precipitation increase yields a mere 0.5% increase in global median drought recovery probability during June‐July‐August, but can lead to a pronounced 6.6% increase if climate gets extremely drier.

Background: The tumor microenvironment (TME) of breast cancer (BRCA) is a complex and dynamic micro-ecosystem that influences BRCA occurrence, progression, and prognosis through its cellular and molecular components. However, as the tumor progresses, the dynamic changes of stromal and immune cells in TME become unclear.Objective: The aim of this study was to identify differentially co-expressed genes (DCGs) associated with the proportion of stromal cells in TME of BRCA, to explore the patterns of cell proportion changes, and ultimately, their impact on prognosis.Methods: A new heuristic feature selection strategy (CorDelSFS) was combined with differential co-expression analysis to identify TME-key DCGs. The expression pattern and co-expression network of TME-key DCGs were analyzed across different TMEs. A prognostic model was constructed using six TME-key DCGs, and the correlation between the risk score and the proportion of stromal cells and immune cells in TME was evaluated.Results: TME-key DCGs mimicked the dynamic trend of BRCA TME and formed cell type-specific subnetworks. The IG gene-related subnetwork, plasmablast-specific expression, played a vital role in the BRCA TME through its adaptive immune function and tumor progression inhibition. The prognostic model showed that the risk score was significantly correlated with the proportion of stromal cells and immune cells in TME, and low-risk patients had stronger adaptive immune function. IGKV1D-39 was identified as a novel BRCA prognostic marker specifically expressed in plasmablasts and involved in adaptive immune responses.Conclusions: This study explores the role of proportionate-related genes in the tumor microenvironment using a machine learning approach and provides new insights for discovering the key biological processes in tumor progression and clinical prognosis.

Food production in much of the world relies on groundwater resources. In many regions, groundwater levels are declining due to a combination of anthropogenic extraction, localized meteorological and geological characteristics, and climate change. Groundwater in India is characteristic of this global trend, with an agricultural sector that is highly dependent on groundwater and increasingly threatened by extraction far in excess of recharge. The complexity of inputs makes groundwater depletion highly heterogeneous across space and time. However, modeling this heterogeneity has thus far proven difficult. Using two ensemble tree-based regression models, we predict district level seasonal groundwater dynamics to an accuracy of R ^2 = 0.4–0.6 and Pearson correlations between 0.6 and 0.8. Further using two high-resolution feature importance methods, we demonstrate that atmospheric humidity, groundwater groundwater-based irrigation, and crop cultivation are the most important predictors of seasonal groundwater dynamics at the district level in India. We further demonstrate a shift in the predictors of groundwater depletion over 1998–2014 that is robustly found between the two feature importance methods, namely increasing importance of deep-well irrigation in Central and Eastern India. These areas coincide with districts where groundwater depletion is most severe. Further analysis shows decreases in crop yields per unit of irrigation over those regions, suggesting decreasing marginal returns for largely increasing quantities of groundwater irrigation used. This analysis demonstrates the public policy value of machine learning models for providing high spatiotemporal accuracy in predicting groundwater depletion, while also highlighting how anthropogenic activity impacts groundwater in India, with consequent implications for productivity and well-being.

Four crisping diets were designed to conduct a feeding experiment to investigate the use of broad bean in the formulated feed of Nile tilapia and optimize its use. The growth performance, muscle characteristics, antioxidant capacity, and intestinal health of Nile tilapia with an initial body weight of 617.32 ± 1.64 g were evaluated after being fed with different diets for 90 days. The results showed no adverse effect on the growth performance of Nile tilapia fed with broad bean diets. Contrastingly, some improvements were found in WGR and SGR, but a lower FCR was obtained. The supplementation of broad beans weakened the antioxidant capacity of fish but did not influence liver health and the immune system. Increasing the amount of broad bean addition can increase muscle quality values, and an embrittling functional package being added to the diet can also improve muscle hardness, adhesiveness, and chewiness of Nile tilapia muscle. In addition, the crisping functional package can be applied to tilapia crisping formulated feed, which helps to improve the integrity of the intestinal tissue structure and optimize the intestinal microbiota of Nile tilapia. The final achievement of this study is to provide a theoretical reference for optimizing the breeding technology of crispy Nile tilapia and developing a specialized crisping diet for the species.

The fastest sea-ice decline has been observed in the western Arctic, but the underlying mechanisms are still unclear. Here, the authors show that the Pacific North American pattern plays an important role in western Arctic sea-ice variability.

Abstract Floods in river deltas are driven by complex interactions between astronomical tides, sea levels, storm surges, wind waves, rainfall‐runoff, and river discharge. Given the anticipated increase in compound flood hazards in river deltas in a warming climate, climate‐informed regional to local extreme water levels (EWLs) is thus critical for decision‐makers to evaluate flood hazards and take adaptation measures. We develop a simple yet computationally efficient stress test framework, which combines historical and projected climatological information and a state‐of‐the‐art hydrodynamic model, to assess future compound coastal‐fluvial flood hazards in river deltas. Our framework is applied in the world's largest single urban area, China's Pearl River Delta (PRD), which is also characterized by densely crossed river network. We find that extreme sea level is the dominant driver causing the compound coastal‐fluvial flood in the PRD over the past 60 years. Meanwhile, there is large spatial heterogeneity of the individual and compound effects of the typhoon intensity, local sea‐level rise, and riverine inflow on coastal‐fluvial floods. In a plausible disruptive scenario (e.g., a 0.50 m sea‐level rise combined with a 9% increase in typhoon intensity in a 2°C warming), the EWL will increase by 0.76 m on average. An additional 1.54 and 0.56 m increase in EWL will occur in the river network and near the river mouth, respectively, if coastal floods coincide with the upstream mean annual flood. Findings from our modeling framework provide important insights to guide adaptation planning in river deltas to withstand future compound floods under climate change.

This study matches data from the China Family Panel Studies (CFPS) with data on the transformation to industrial artificial intelligence (AI) in cities to explore the effect of this transformation on workers' mental health and its underlying mechanisms in China. The findings show the following (1). The transformation to industrial AI effectively alleviates multiple mental health problems and improves workers' mental health (2). Work intensity and wage income play an intermediary role in the relationship between the industrial AI transformation and workers' mental health (3). Potential endogeneity problems in the relationship between industrial AI and workers' mental health are considered, and robustness tests are conducted (including changing the dependent variables, independent variables and regression models). The main results and impact mechanisms remain robust and reliable. This study extends the research on the relationship between industrial AI and workers' health, which has important theoretical implications. Additionally, based on the Chinese context, this research has important implications for the current AI transformation in developing countries. Transition economies with labor shortages can achieve a win-win situation by promoting industrial AI to fill the labor gap and improve workers' mental health.

The role of solar and wind energy (SWE) in management of water-food-energy (WFE) nexus is largely neglected. Here the authors developed a trade-off frontier framework to quantify the water sustainability value of SWE and applied it in California, where they found that SWE penetration creates beneficial feedback for the WFE nexus by enhancing drought resilience and benefits groundwater sustainability over long run.

Context: Pyrolysis has been considered an effective bioenergy technology to produce renewable energy; however, the extent to which it mitigates climate change is greatly influenced by the usage of its by-product, biochar. Bioenergy development relies on the stable supply of agricultural commodities; however, climate-induced impacts on crop yields that potentially influence the stability of biomass supply must be investigated. Objective: The current study analyzed Taiwan’s bioenergy potential and the associated economic and environmental consequences. Materials and methods: A two-stage stochastic programming with a recourse model was employed to explore the biofuel and biopower production under various climate impact possibilities. Furthermore, emission offsets, such as CO2 reduction from bioenergy production and N2O reduction from biochar application were elucidated, along with changes in cropping patterns. Results: The results indicated that: (1) with onsite biochar application, up to 8 82 000 metric tons of CO2-equivalent emissions can be offset from the reduction of fertilizer use; (2) ethanol is less competitive, with more than 99% of bioenergy is derived from pyrolysis-based biopower; (3) net offset potential substantially decreases when N2O emissions from cropping land are considered; and (4) biochar can contribute up to 42.43–51.02% of the total emission sequestration. Discussion and conclusion: The net impact of promotion policies on social welfare depends on the size of gains obtained by producers and losses experienced by consumers, and researchers need to incorporate the heterogeneous properties of these policies to determine the country-specific effects of bioenergy development on social welfare.

With global warming, precipitation events are often prone to intensify in some regions. Understanding the changing characteristics of annual and daily extreme precipitation as well as the underlying mechanisms plays an import role for early warning of precipitation-induced disaster (e.g. floods, landslides) and water resources management, especially in densely populated urban areas. In this study, we investigate the long-term trend of annual and daily extreme precipitation in China during 1960–2010 based on daily observations from 539 meteorological stations, and the land cover map with impervious information. We find an overall increasing trend in annual and daily extreme precipitation, particularly in South-East and North-West of China. Moreover, 157 stations located in metropolitan regions experience higher increasing trends of daily extreme precipitation, particularly in Shanghai and Guangzhou metropolitan areas. It is noted that the central urban area of one metropolitan region may have significantly higher increasing trends of daily extreme precipitation than corresponding surrounding areas.

The impacts of anthropogenic land use and land cover (LULC) changes on the spatiotemporal distribution of precipitation in megacities have been highlighted in studies on urban climate change. In this study, we conducted a quantitative analysis of urban growth on the impact on precipitation in Shanghai, China. We considered four periods of LULC data in 1979, 1990, 2000 and 2010, in addition to the long-term (1979–2010) trend of daily precipitation. The results indicate that the trend in precipitation exhibit different characteristics for urban (Ur), outskirt of urban (OUr) and outer suburb (OS) regions. Most Ur regions had an upward trend in annual and extreme precipitation during 1979–2010, while annual precipitation for the OUr and OS regions exhibited a decreasing trend. From 1979 to 2010, the areas of fastest expansion were located in the OUr region. The OS region, far away from the central area, had a relatively lower rate of change. In addition, OUr regions with rapid LULC changes exhibited higher increasing trends in annual and daily extreme precipitation, which is critical for the identification of frequent precipitation areas and the reliable projection of further changes.

Existing gauging networks are sparse and not readily available in real-time over the transboundary Lancang−Mekong River (LMR) basin, making it difficult to accurately identify drought. In this study, we aimed to build an operational real-time Lancang−Mekong drought monitor (LMDM), through combining satellite real-time data and the Variable Infiltration Capacity (VIC) hydrological model at a 0.25° spatial resolution. Toward this, three VIC runs were conducted: (1) a 60-year (1951−2010) historical simulation driven by Princeton’s global meteorological forcing (PGF) for yielding ‘normal’ conditions (PGF-VIC), wherein the VIC was calibrated with 20-year observed streamflow at six hydrological stations; (2) a short-period (2011−2014) simulation to bridge the gap between the historical and the real-time modeling; (3) the real-time (2015−present) simulation driven by bias-corrected satellite data, wherein the real-time soil moisture (SM) estimate was expressed as percentile (relative to the ‘normal’) for drought monitoring. Results show that VIC can successfully reproduce the observed hydrographs, with the Nash−Sutcliffe efficiency exceeding 0.70 and the relative bias mostly within 15%. Assessment on the performance of LMDM shows that the real-time SM estimates bear good spatial similarity to the reference, with the correlation coefficient beyond 0.80 across >70% of the domain. In terms of drought monitoring, the LMDM can reasonably reproduce the two recorded droughts, implying extreme droughts covering the Lower LMR during 2004/05 and widespread severe 2009/10 drought across the upper domain. The percentage drought area implied by the LMDM and the reference is close, corresponding to 66% and 60%, 43% and 40%, and 44% and 36% for each typical drought month. Since January 2015, the LMDM was running in an operational mode, from which the 2016 unprecedented drought was successfully identified in Mekong Delta. This study highlights the LMDM’s capability for reliable real-time drought monitoring, which can serve as a valuable drought early warning prototype for other data-poor regions.

Satellite-observed Earth’s greening has been reproduced by the latest generation of Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Land evapotranspiration (ET) is expected to rise with increasing leaf area index (LAI, Earth’s greening). The responses of ET play a key role in the land–climate interaction, but they have not been evaluated previously. Here, we assessed the responses of ET to Earth’s greening in these CMIP5 ESMs. We verified a significant and positive response of ET to the modeled greening in each model. However, the responses were not comparable across the ESMs because of an inherent bias in the sensitivity of ET to LAI $(\partial {\rm{E}}{\rm{T}}/\partial {\rm{L}}{\rm{A}}{\rm{I}})$ in the models: $\partial {\rm{E}}{\rm{T}}/\partial {\rm{L}}{\rm{A}}{\rm{I}}$ is precisely and inversely proportional to the trend of LAI $(\partial {\rm{L}}{\rm{A}}{\rm{I}}/\partial t)$ across the ESMs. Constrained by this inversely proportional relationship with the satellite-observed $\partial {\rm{L}}{\rm{A}}{\rm{I}}/\partial t,$ the Earth’s $\partial {\rm{E}}{\rm{T}}/\partial {\rm{\text{LAI}}}$ is 0.26 (0.21–0.34) mm d ^−1 per m ^2 m ^−2 , equaling the independent estimates from satellite-derived reconstructions of ET and LAI. Thus, the Earth’s greening-induced acceleration of ET is about 11.4 mm yr ^−1 , accounting for more than 50% of the observed increase in land ET over the last 30 years. To better model the land–climate interaction, $\partial {\rm{E}}{\rm{T}}/\partial {\rm{L}}{\rm{A}}{\rm{I}}$ in these ESMs should be calibrated. A feasible means is to improve the representation of the magnitude of LAI in these CMIP5 ESMs.

Limiting global warming to 2 °C requires urgent action on land-based mitigation. This study evaluates the biogeochemical and biogeophysical implications of two alternative land-based mitigation scenarios that aim to achieve the same radiative forcing. One scenario is primarily driven by bioenergy expansion (SSP226Lu-BIOCROP), while the other involves re/afforestation (SSP126Lu-REFOREST). We find that overall, SSP126Lu-REFOREST is a more efficient strategy for removing CO2 from the atmosphere by 2100, resulting in a net carbon sink of 242 ~ 483 PgC with smaller uncertainties compared to SSP226Lu-BIOCROP, which exhibits a wider range of -78 ~ 621 PgC. However, SSP126Lu-REFOREST leads to a relatively warmer planetary climate than SSP226Lu-BIOCROP, and this relative warming can be intensified in certain re/afforested regions where local climates are not favorable for tree growth. Despite the cooling effect on a global scale, SSP226Lu-BIOCROP reshuffles regional warming hotspots, amplifying summer temperatures in vulnerable tropical regions such as Central Africa and Southeast Asia. Our findings highlight the need for strategic land use planning to identify suitable regions for re/afforestation and bioenergy expansion, thereby improving the likelihood of achieving the intended climate mitigation outcomes. [ABSTRACT FROM AUTHOR]

Background To estimate the global trends and disease burden of Clostridioides difficile infection (CDI) and its correlation with worldwide antibiotic consumption. Methods Clostridioides difficile infection and antibiotic consumption data were retrieved from the Global Burden of Disease 2019, ResistanceMap-AntibiocUse, Food and Drug Administration (FDA) Adverse Event Reporting System, and Global Antimicrobial Resistance and Use Surveillance System. Joint-point regression and age-period-cohort model were developed to show the global trends and burden of CDI. Correlation tests were calculated to explore the relationship between CDI and antibiotics. Results Globally, CDI is the most significant one with a high-rocketing burden increase rate among 13 pathogens causing diarrheal deaths and disability-adjusted life years (DALYs). The age-standardised death rate (ASDR) increased from 0.19 in 1990 to 0.43 in 2019, in which the elderly and females are at higher risk. A rapid increase in ASDR in high to middle sociodemographic index (SDI) regions such as North America (average annual percentage change (AAPC)=7.71%), Andean (AAPC=7.82%), and Southern Latin America (AAPC=11.08%) was identified. Antibiotic consumption has a significant positive correlation with CDI with different risk stratifications. Conclusions The global burden of CDI has continuously increased for the past 30 years, especially in high to middle-SDI regions. World antibiotic consumption showed a strong positive correlation with CDI with different risk stratification. More effective prevention and control measures should be implemented in these critical regions, with a specific emphasis on vulnerable populations, to mitigate the spread of epidemics. [ABSTRACT FROM AUTHOR]