Risk factors related to low-level viraemia in chronic hepatitis B patients receiving entecavir treatment.

Background: About 20% of on-treatment patients with chronic hepatitis B (CHB) experienced low-level viraemia (LLV), which is associated with persistent low-grade inflammation, fibrosis progression, and increased risk of hepatocellular carcinoma. We aimed to investigate the high-risk factors related to LLV. Methods: In this retrospective study, patients receiving entecavir (ETV) treatment from January 2018 to January 2023 were enrolled, and were divided into a LLV (HBV DNA 20-2000 IU/mL) cohort and a complete virological response (CVR) (HBV DNA < 20 IU/mL) cohort according to the virological response at week 48 posttreatment. Treatment baseline characteristics were retrieved from electronic medical records. Multivariate logistic regression was performed. Results: Totally, 1653 patients were enrolled, male patients accounted for 73.0%; the median age was 44 years; the mean HBV DNA level was 5.9 Log10 IU/ml. Among them, 472 (28.6%) experienced LLV. Multivariate analysis showed that HBeAg positivity (OR = 2.650, 95% CI: 2.000-3.511, p < 0.001), HBV DNA ≥ 6.0 Log10 IU/mL (OR = 1.370, 95% CI: 1.054-1.780, p = 0.019), qHBsAg ≥ 9000 IU/mL (OR = 4.472, 95% CI: 3.410-5.866, p < 0.001), cirrhosis (OR = 1.650, 95% CI: 1.234-2.207, P = 0.001), LSM ≥ 13.0 kPa (OR = 1.644, 95% CI: 1.203-2.246, p = 0.002), and PLT < 100×109/L (OR = 1.450, 95% CI: 1.094-1.922, p = 0.010) at baseline were related to the development of LLV. Conclusions: High HBV DNA/HBsAg quantification/LSM, low PLT, HBeAg positivity, and liver cirrhosis were high-risk factors associated with LLV in patients receiving entecavir treatment.

Transcriptome-metabolome reveals the molecular changes in meat production and quality in the hybrid populations of Sichuan white goose.

Hybrid breeding has proven to enhance meat quality and is extensively utilized in goose breeding. Nevertheless, there is a paucity of research investigating the molecular mechanisms that underlie the meat quality of hybrid geese. In this study, we employed the Sichuan White Goose as the maternal line for hybridization with the Zhedong White Goose and Tianfu Meat Goose P3 line. We assessed the growth and slaughter meat quality performance of 10-wk-old hybrid offspring in comparison to Sichuan white goose purebred offspring. The results indicate that hybrid geese have significantly improved performance in growth and slaughter meat quality. Furthermore, we conducted a comprehensive analysis of the chest muscles of hybrid offspring through transcriptomics and metabolomics to unravel the effects of hybrid breeding on growth and meat quality. A total of 673 differentially expressed genes (DEGs), and 93 differentially expressed metabolites were identified. The joint analysis highlighted the significant enrichment of DEGs AMPD1, AMPD3, RRM2, ENTPD3, and the metabolite UMP in the nucleotide metabolism pathway. These findings underscore the crucial role of these genetic and metabolic factors in regulating muscle growth and meat quality in hybrid populations.

Dynamics of land cover changes and carbon emissions driven by large dams in China.

The recent surge in dam construction has sparked debates regarding their contribution to carbon neutrality and food security, focusing on trade-offs between production benefits and ecological drawbacks. However, how dams affect carbon emissions and land cover changes, including their spatial differentiations, remains unclear. We quantified spatiotemporal variations in carbon emissions and storage of 137 large dams in China from 1992 to 2020, resulting from land cover change in potentially affected areas. We observed a lesser increase in carbon emissions and a more pronounced increase in carbon storage driven by forest conservation and regeneration within dam-affected areas compared to unaffected areas. Additionally, we noticed an increased grain yield in nearby areas potentially due to increased water availability. Our findings highlight the importance of considering land cover change when assessing carbon neutrality or grain yield at regional and national scales. This study provides useful insights into optimizing dam locations to mitigate future carbon emissions effectively.

Impacts of Spatial Resolution and XCO2 Precision on Satellite Capability for CO2 Plumes Detection.

Greenhouse gas satellites can provide consistently global CO2 data which are important inputs for the top-down inverse estimation of CO2 emissions and their dynamic changes. By tracking greenhouse gas emissions, policymakers and businesses can identify areas where reductions are needed most and implement effective strategies to reduce their impact on the environment. Monitoring greenhouse gases provides valuable data for scientists studying climate change. The requirements for CO2 emissions monitoring and verification support capacity drive the payload design of future CO2 satellites. In this study, we quantitatively evaluate the performance of satellite in detecting CO2 plumes from power plants based on an improved Gaussian plume model, with focus on impacts of the satellite spatial resolution and the satellite-derived XCO2 precision under different meteorological conditions. The simulations of CO2 plumes indicate that the enhanced spatial resolution and XCO2 precision can significantly improve the detection capability of satellite, especially for small-sized power plants with emissions below 6 Mt CO2/yr. The satellite-detected maximum of XCO2 enhancement strongly varies with the wind condition. For a satellite with a XCO2 precision of 0.7 ppm and a spatial resolution of 2 km, it can recognize a power plant with emissions of 2.69 Mt CO2/yr at a wind speed of 2 m/s, while its emission needs be larger than 5.1 Mt CO2/yr if the power plant is expected to be detected at a wind speed of 4 m/s. Considering the uncertainties in the simulated wind field, the satellite-derived XCO2 measurements and the hypothesized CO2 emissions, their cumulative contribution to the overall accuracy of the satellite's ability to identify realistic enhancement in XCO2 are investigated in the future. The uncertainties of ΔXCO2 caused by the uncertainty in wind speed is more significant than those introduced from the uncertainty in wind direction. In the case of a power plant emitting 5.1 Mt CO2/yr, with the wind speed increasing from 0.5 m/s to 4 m/s, the simulated ΔXCO2 uncertainty associated with the wind field ranges from 3.75 ± 2.01 ppm to 0.46 ± 0.24 ppm and from 1.82 ± 0.95 ppm to 0.22 ± 0.11 ppm for 1 × 1 km2 and 2 × 2 km2 pixel size, respectively. Generally, even for a wind direction with a higher overall uncertainty, satellite still has a more effective capability for detecting CO2 emission on this wind direction, because there is more rapid growth for simulated maximal XCO2 enhancements than that for overall uncertainties. A designed spatial resolution of satellite better than 1 km and a XCO2 precision higher than 0.7 ppm are suggested, because the CO2 emission from small-sized power plants is much more likely be detected when the wind speed is below 3 m/s. Although spatial resolution and observed precision parameters are not sufficient to support the full design of future CO2 satellites, this study still can provide valuable insights for enhancing satellite monitoring of anthropogenic CO2 emissions.

The influence of green human resource management on employees' green innovation behavior: The role of green organizational commitment and knowledge sharing.

Employees' green innovation behavior is a key factor in enterprises' promotion of green development strategies. Combining social information processing and social exchange theory, this article considers workers' green organizational commitment (GOC) as a mediating construct and knowledge sharing as a moderating construct and establishes a model of the influence of green human resource management (GHRM) on workers' green innovation conduct in company settings. An empirical analysis of 436 employees from 28 high-tech enterprises in Fujian Province shows that GHRM has a positive significant bearing on workers' green innovation conduct. Employees' GOC has a positive mediating influence on the interaction between GHRM and employees' green innovation conduct. Employees' knowledge sharing moderates positively the interaction between green organizational commitment and green innovation behavior and moderates the mediating effect of green organizational commitment. Therefore, we propose stimulating employees' green innovation behavior by implementing green GHRM, focusing on green demand and creating a knowledge-sharing atmosphere.

Anticipated Regret, Entrepreneurial Cognition, and Entrepreneurial Persistence.

Based on the regret regulation theory and entrepreneurial cognition theory, this study aims to investigate the relationship between entrepreneurs' anticipated regret and entrepreneurial persistence through the mediator of entrepreneurial cognition. To that end, we distributed surveys to entrepreneurs who were supported by the "Xing Chuang Tian Di" project and used 248 questionnaire data to examine the hypotheses. The results show that entrepreneurs' anticipated regret has a significant and direct impact on entrepreneurial persistence. Also, entrepreneurship cognition plays a mediating role between anticipated regret and entrepreneurial persistence. In addition, the entrepreneurial environment plays a positive role in moderating the relationship between anticipated regret and entrepreneurial persistence.

Analysis of flow loss characteristics of slanted axial-flow pump device based on entropy production theory.

Slanted axial-flow pump devices are widely applied in urban water supply, irrigation and drainage engineering fields. The second law of thermodynamics is applied to investigate the flow loss characteristics of the 30° slanted axial-flow pump model according to the flow loss analysis method of entropy production theory, so that the hydraulic loss characteristics can be revealed in internal flow process of the slanted axial-flow pump. The three-dimensional numerical simulation of the whole flow conduit in slanted axial-flow pump was conducted and the entropy production increased in the flow process was calculated. The location and distribution characteristics of the flow loss of the pump were qualitatively analysed. The results show that the entropy production in impeller is the highest among the pump components. With the increase of flow rate, the proportion of the entropy production in impeller in total value of the pump device increases continuously. The wall entropy production of impeller, guide vane and outlet conduit are lower than the mainstream entropy production, and the mainstream entropy production occupies the dominant position. As the flow rate grows, the proportion of turbulent dissipation entropy production decreases, and the proportion of wall dissipation entropy production increases. At 0.8Q bep, the proportion of turbulent dissipation entropy production is close to 74%, which is about 2.8 times that of wall entropy production. Under 1.2Q bep condition, the proportion of turbulent dissipation entropy production is just 5.5% higher than that of wall dissipation entropy production.

The LAC Score Indicates Significant Fibrosis in Patients With Chronic Drug-Induced Liver Injury: A Large Biopsy-Based Study.

Currently, there are no satisfactory noninvasive methods for the diagnosis of fibrosis in patients with chronic drug-induced liver injury (DILI). Our goal was to develop an algorithm to improve the diagnostic accuracy of significant fibrosis in this population. In the present study, we retrospectively investigated the biochemical and pathological characteristics of consecutive patients with biopsy-proven chronic DILI, who presented at our hospital from January 2013 to December 2017. A noninvasive algorithm was developed by using multivariate logistic regression, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) to diagnose significant fibrosis in the training cohort, and the algorithm was subsequently validated in the validation cohort. Totally, 1,130 patients were enrolled and randomly assigned into a training cohort (n = 848) and a validation cohort (n = 282). Based on the multivariate analysis, LSM, CHE, and APRI were independently associated with significant fibrosis. A novel algorithm, LAC, was identified with the AUROC of 0.81, which was significantly higher than LSM (AUROC 0.78), CHE (AUROC 0.73), and APRI (AUROC 0.68), alone. The best cutoff value of LAC in the training cohort was 5.4. When the LAC score was used to diagnose advanced fibrosis and cirrhosis stages, the optimal cutoff values were 6.2 and 6.7, respectively, and the AUROC values were 0.84 and 0.90 in the training cohort and 0.81 and 0.83 in the validation cohort. This study proved that the LAC score can contribute to the accurate assessment of high-risk disease progression and the establishment of optimal treatment strategies for patients with chronic DILI.

Switching from entecavir to tenofovir alafenamide for chronic hepatitis B patients with low-level viraemia.

BACKGROUND AND AIMS: About 20% of patients receiving nucleos(t)ide analogues treatment experienced low-level viraemia (LLV), which is associated with progression of liver fibrosis and high risk of hepatocellular carcinoma. We aimed to evaluate the effectiveness and safety of switching from entecavir (ETV) to tenofovir alafenamide fumarate (TAF) in ETV-treated patients with LLV. METHODS: In this prospective study, ETV-treated patients with LLV, presented to our hospital from December 2018 to October 2019, were enrolled. Switching to TAF or continuing ETV was given. The primary effectiveness endpoint was complete virological response (CVR) at 24 weeks, and the safety endpoint was the first occurrence of any clinical adverse event during the treatment. RESULTS: Totally, 211 patients were recruited and propensity score matching (PSM) generated 75 patients in either TAF or ETV group. After PSM, baseline characteristics were balanced in two groups. After 24-week treatment, the CVR and ALT normalization in TAF group were 62.7% and 47.6%, which were higher than 9.3% and 10.5% in ETV group (OR 16.4, 95% CI 6.6-40.0, P < .001) respectively. Subgroup analysis showed that switching to TAF achieved favours CVR regardless of the status of sex, age, CHB family history, HBV DNA, HBeAg and cirrhosis, whereas alcohol consumption and diabetes mellitus might compromise the CVR of switching to TAF. Both therapies were well tolerated and had satisfying renal safety. CONCLUSIONS: For ETV-treated patients with LLV, switching to TAF is safe enough and superior compared with continuing ETV monotherapy regarding both virological and biochemical benefits.

A QoT prediction technique based on machine learning and NLSE for QoS and new lightpaths in optical communication networks.

In this paper, we proposed a quality of transmission (QoT) prediction technique for the quality of service (QoS) link setup based on machine learning classifiers, with synthetic data generated using the transmission equations instead of the Gaussian noise (GN) model. The proposed technique uses some link and signal characteristics as input features. The bit error rate (BER) of the signals was compared with the forward error correction threshold BER, and the comparison results were employed as labels. The transmission equations approach is a better alternative to the GN model (or other similar margin-based models) in the absence of real data (i.e., at the deployment stage of a network) or the case that real data are scarce (i.e., for enriching the dataset/reducing probing lightpaths); furthermore, the three classifiers trained using the data of the transmission equations are more reliable and practical than those trained using the data of the GN model. Meanwhile, we noted that the priority of the three classifiers should be support vector machine (SVM) >K nearest neighbor (KNN) > logistic regression (LR) as shown in the results obtained by the transmission equations, instead of SVM > LR > KNN as in the results of the GN model.

Evaluation of Landsat-8 and Sentinel-2A aerosol optical depth retrievals across Chinese cities and implications for medium spatial resolution urban aerosol monitoring.

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. A year of 30 m Landsat-8 and 10 m Sentinel-2A AOD data retrieved using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities. Stringent selection criteria were used to select contemporaneous data - only satellite and AERONET data acquired within 10 minutes were considered. The average satellite retrieved AOD over a 1470 m × 1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r2 > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research. The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed.

Heavily Graphitic-Nitrogen Self-doped High-porosity Carbon for the Electrocatalysis of Oxygen Reduction Reaction.

Large-scale production of active and stable porous carbon catalysts for oxygen reduction reaction (ORR) from protein-rich biomass became a hot topic in fuel cell technology. Here, we report a facile strategy for synthesis of nitrogen-doped porous nanocarbons by means of a simple two-step pyrolysis process combined with the activation of zinc chloride and acid-treatment process, in which kidney bean via low-temperature carbonization was preferentially adopted as the only carbon-nitrogen sources. The results show that this carbon material exhibits excellent ORR electrocatalytic activity, and higher durability and methanol-tolerant property compared to the state-of-the-art Pt/C catalyst for the ORR, which can be mainly attributed to high graphitic-nitrogen content, high specific surface area, and porous characteristics. Our results can encourage the synthesis of high-performance carbon-based ORR electrocatalysts derived from widely-existed natural biomass.

Surface Modification of Multi-Walled Carbon Nanotubes via Hemoglobin-Derived Iron and Nitrogen-Rich Carbon Nanolayers for the Electrocatalysis of Oxygen Reduction.

The great challenge of boosting the oxygen reduction reaction (ORR) activity of non-noble-metal electrocatalysts is how to achieve effective exposure and full utilization of nitrogen-rich active sites. To realize the goals of high utilization of active sites and fast electron transport, here we report a new strategy for synthesis of an iron and nitrogen co-doped carbon nanolayers-wrapped multi-walled carbon nanotubes as ORR electrocatalyst (N-C@CNT-Fe) via using partially carbonized hemoglobin as a single-source precursor. The onset and half-wave potentials for ORR of N-C@CNT-Fe are only 45 and 54 mV lower than those on a commercial Pt/C (20 wt.% Pt) catalyst, respectively. Besides, this catalyst prepared in this work has been confirmed to follow a four-electron reaction mechanism in ORR process, and also displays ultra-high electrochemical cycling stability in both acidic and alkaline electrolytes. The enhancement of ORR activity can be not only attributed to full exposure and utilization of active site structures, but also can be resulted from the improvement of electrical conductivity owing to the introduction of CNT support. The analysis of X-ray photoelectric spectroscopy shows that both Fe-N and graphitic-N species may be the ORR active site structures of the prepared catalyst. Our study can provide a valuable idea for effective improvement of the electrocatalytic activity of non-noble-metal ORR catalysts.

The Oxygen Reduction Electrocatalytic Activity of Cobalt and Nitrogen Co-doped Carbon Nanocatalyst Synthesized by a Flat Template.

The design of noble-metal-free catalysts for oxygen reduction reaction (ORR) is very important to the commercialization of fuel cells. Here, we use a Co-modified montmorillonite (Co-MMT) as a flat template to prepare Co- and N-doped nanocarbon ORR catalysts derived from carbonization of polyaniline at controlled temperatures. The use of flat template can hinder the agglomeration of polyaniline during pyrolysis process and optimize the N-rich active site density on the surface. The addition of transition metal Co in the flat MMT template can largely promote the formation of Co-N sites in prepared catalyst, facilitating the effective improvement of catalytic activity towards the ORR with a direct four-electron transfer pathway. The excellent ORR activity may be mainly attributed to high contents of graphitic N, pyridinic-N, and Co-N configurations. This study opens a new way to rationally design cheap and active ORR catalysts by using simple flat compound as a direct template.

A general method for parameter estimation in light-response models.

Selecting appropriate initial values is critical for parameter estimation in nonlinear photosynthetic light response models. Failed convergence often occurs due to wrongly selected initial values when using currently available methods, especially the kind of local optimization. There are no reliable methods that can resolve the conundrum of selecting appropriate initial values. After comparing the performance of the Levenberg-Marquardt algorithm and other three algorithms for global optimization, we develop a general method for parameter estimation in four photosynthetic light response models, based on the use of Differential Evolution (DE). The new method was shown to successfully provide good fits (R(2) > 0.98) and robust parameter estimates for 42 datasets collected for 21 plant species under the same initial values. It suggests that the DE algorithm can efficiently resolve the issue of hyper initial-value sensitivity when using local optimization methods. Therefore, the DE method can be applied to fit the light-response curves of various species without considering the initial values.

A Nanopore-Structured Nitrogen-Doped Biocarbon Electrocatalyst for Oxygen Reduction from Two-Step Carbonization of Lemna minor Biomass.

So far, the development of highly active and stable carbon-based electrocatalysts for oxygen reduction reaction (ORR) to replace commercial Pt/C catalyst is a hot topic. In this study, a new nanoporous nitrogen-doped carbon material was facilely designed by two-step pyrolysis of the renewable Lemna minor enriched in crude protein under a nitrogen atmosphere. Electrochemical measurements show that the onset potential for ORR on this carbon material is around 0.93 V (versus reversible hydrogen electrode), slightly lower than that on the Pt/C catalyst, but its cycling stability is higher compared to the Pt/C catalyst in an alkaline medium. Besides, the ORR at this catalyst approaches to a four-electron transfer pathway. The obtained ORR performance can be basically attributed to the formation of high contents of pyridinic and graphitic nitrogen atoms inside this catalyst. Thus, this work opens up the path in the ORR catalysis for the design of nitrogen-doped carbon materials utilizing aquatic plants as starting precursors.

Easy conversion of protein-rich enoki mushroom biomass to a nitrogen-doped carbon nanomaterial as a promising metal-free catalyst for oxygen reduction reaction.

The search for low-cost, highly active, and stable catalysts to replace the Pt-based catalysts for oxygen reduction reaction (ORR) has recently become a topic of interest. Herein, we report a new strategy to design a nitrogen-doped carbon nanomaterial for use as a metal-free ORR catalyst based on facile pyrolysis of protein-rich enoki mushroom (Flammulina velutipes) biomass at 900 °C with carbon nanotubes as a conductive agent and inserting matrix. We found that various forms of nitrogen (nitrile, pyrrolic and graphitic) were incorporated into the carbon molecular skeleton of the product, which exhibited more excellent ORR electrocatalytic activity and better durability in alkaline medium than those in acidic medium. Remarkably, the ORR half-wave potential measured on our material was around 0.81 V in alkaline medium, slightly lower than that on the commercial 20 wt% Pt/C catalyst (0.86 V). Meanwhile, the ORR followed the desired 4-electron transfer mechanism involving the direct reduction pathway. The ORR performance was also markedly better than or at least comparable to the leading results in the literature based on biomass-derived carbon-based catalysts. Besides, we significantly proposed that the graphitic-nitrogen species that is most responsible for the ORR activity can function as the electrocatalytically active center for ORR, and the pyrrolic-nitrogen species can act as an effective promoter for ORR only. The results suggested a promising route based on economical and sustainable fungi biomass towards the large-scale production of valuable carbon nanomaterials as highly active and stable metal-free catalysts for ORR under alkaline conditions.

MiR-22 Suppresses BMP7 in the Development of Cirrhosis.

BACKGROUND/AIMS: New strategies for the prevention and treatment of cirrhosis are urgently needed for improving therapeutic outcome. A role of microRNAs (miRNAs) in the pathogenesis of cirrhosis has been recently acknowledged, whereas the exact involved miRNAs as well as the associated molecular signaling pathways have not been determined. Specifically, the studies on the relationship between miR-22 and bone morphogenic protein 7 (BMP7) in the development of cirrhosis are lacking. METHODS: We examined the correlation of the levels of miR-22 and bone morphogenic protein 7 (BMP7) in the liver biopsies from patients with cirrhosis. We examined overexpression or suppression of miR-22 on BMP7 in hepatocytes. We examined the binding of miR-22 to the 3'-UTR of BMP7 mRNA. Finally, in a carbon tetrachloride (CCl4)-induced cirrhosis model in mice, we gave mice adeno-associated viruses carrying antisense of miR-22, and examined its effects on BMP7 levels and the hallmarks of cirrhosis. RESULTS: The levels of miR-22 and BMP7 in the liver biopsies from patients were strongly and inversely correlated. MiR-22 inhibited BMP7 expression in hepatocytes, through directly binding the 3'-UTR of BMP7 mRNA. Expression of antisense miR-22 significantly attenuated the levels of liver fibrosis, portal hypertension and sodium retention caused by CCl4, possibly through upregulation of BMP7. CONCLUSIONS: MiR-22 promotes the development of cirrhosis through BMP7 suppression.