Transforming Research on Recreational Ecosystem Services into Applications and Governance

The science-practice gap has recently been discussed as a critical challenge restricting sustainable growth and development in all facets of our society, including explorations of Recreation Ecosystem Services (RES). To better explore how well the scientific study of RES and its application are connected, this paper aims to synthesize empirical evidence based on an in-depth and systematic literature review. We found that studies of RES have not effectively transformed into the decision-making and long-term planning of our cities. From 2005 to 2020, only 13% of studies referred to specific applications, and about 40% of papers mentioned no applications or practical implications for their research. However, RES research has many potential applications, which can be categorised into six main aspects. In terms of non-spatial improvement: Improved monetary benefits (40%), non-monetary benefits (30%); in terms of spatial improvement: space with high recreational potential or degradation (7%), the relation between supply and demand (7%); and Cross-service governance (16%). After combining the results of various studies, we developed a framework starting from applicable problems and their solutions, which can incorporate the outcomes of RES research while systematically narrowing down the research questions and methods. The framework offers a starting point for further research that can modify and improve in bridging science-practice gaps in RES studies.National Natural Science Foundation of ChinaPeer Reviewe

Coronary artery bypass grafting vs. percutaneous coronary intervention in coronary artery disease patients with advanced chronic kidney disease: A Chinese single-center study

ObjectivesAims to compare the contemporary and long-term outcomes of coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) in coronary artery disease (CAD) patients with advanced chronic kidney disease (CKD).Methods823 CAD patients with advanced CKD (eGFR < 30 ml/min/1.73 m2) were collected, including 247 patients who underwent CABG and 576 patients received PCI from January 2014 to February 2021. The primary endpoint was all-cause death. The secondary endpoints included major adverse cardiac and cerebrovascular events (MACCEs), myocardial infarction (MI), stroke and revascularization.ResultsMultivariable Cox regression models were used and propensity score matching (PSM) was also performed. After PSM, the 30-day mortality rate in the CABG group was higher than that in the PCI group but without statistically significant (6.6% vs. 2.4%, p = 0.24). During the first year, patients referred for CABG had a hazard ratio (HR) of 1.42 [95% confidence interval (CI), 0.41–3.01] for mortality compared with PCI. At the end of the 5-year follow-up, CABG group had a HR of 0.58 (95%CI, 0.38–0.86) for repeat revascularization, a HR of 0.77 (95%CI, 0.52–1.14) for survival rate and a HR of 0.88(95%CI, 0.56–1.18) for MACCEs as compared to PCI.ConclusionsAmong patients with CAD and advanced CKD who underwent CABG or PCI, the all-cause mortality and MACCEs were comparable between the two groups in 30 days, 1-year and 5 years. However, CABG was only associated with a significantly lower risk for repeat revascularization compared with PCI at 5 years follow-up

Loss of N-Linked Glycosylation from the Hemagglutinin- Neuraminidase Protein Alters Virulence of Newcastle Disease Virus

The hemagglutinin-neuraminidase (HN) protein of Newcastle disease virus (NDV) is an important determinant of its virulence. We investigated the role of each of the four functional N-linked glycosylation sites (G1 to G4) of the HN glycoprotein of NDV on its pathogenicity. The N-linked glycosylation sites G1 to G4 at residues 119, 341, 433, and 481, respectively, of a moderately pathogenic NDV strain Beaudette C (BC) were eliminated individually by site-directed mutagenesis on a full-length cDNA clone of BC. A double mutant (G12) was also created by eliminating the first and second glycosylation sites at residues 119 and 341, respectively. Infectious virus was recovered from each of the cDNA clones of the HN glycoprotein mutants, employing a reverse genetics technique. There was a greater delay in the replication of G4 and G12 mutant viruses than in the parental virus. Loss of glycosylation does not affect the receptor recognition by HN glycoprotein of NDV. The neuraminidase activity of G4 and G12 mutant viruses and the fusogenicity of the G4 mutant virus were significantly lower than those of the parental virus. The fusogenicity of the double mutant virus (G12) was significantly higher than that of the parental virus. Cell surface expression of the G4 virus HN was significantly lower than that of the parental virus. The antigenic reactivities of the mutants to a panel of monoclonal antibodies against the HN protein indicated that removal of glycosylation from the HN protein increased (G1, G3, and G12) or decreased (G2 and G4) the formation of antigenic sites, depending on their location. In standard tests to assess virulence in chickens, all of the glycosylation mutants were less virulent than the parental BC virus, but the G4 and G12 mutants were the least virulent

SIRT4 functions as a tumor suppressor during prostate cancer by inducing apoptosis and inhibiting glutamine metabolism

Abstract Localized in the mitochondria, SIRT4 is a nicotinamide adenine dinucleotide (NAD +) -dependent adenosine diphosphate (ADP) -ribosyltransferase and is one of the least characterized members of the sirtuin family. Although it is well known that it shows deacetylase activity for energy metabolism, little is understood about its function in tumorigenesis. Recent research suggests that SIRT4 may work as both a tumor suppressor gene and an oncogene. However, the clinical significance of SIRT4 in prostate cancer remains unknown. In this study, we evaluated SIRT4 protein levels in cancerous prostate tissue and corresponding non-tumor prostate tissue via immunohistochemical staining on a tissue microarray including tissues from 89 prostate cancer patients. The association between SIRT4 expression and Gleason score was also determined. Further, shSIRT4 or stable prostate cancer cell lines (22RV1) overexpressing SIRT4 were constructed via lentiviral infection. Using Cell-Counting Kit-8 (CCK-8) assay, wound healing assay, migration, and invasion and apoptosis assays, the effects of SIRT4 on the migration, invasion ability, and proliferation of prostate cancer cells were investigated. We also determined the effect of SIRT4 on glutamine metabolism in 22RV1 cells. We found the protein levels of SIRT4 in prostate cancer tissues were significantly lower than those in their non-neoplastic tissue counterparts (P < 0.01); a lower SIRT4 level was also significantly associated with a higher Gleason score (P < 0.01). SIRT4 suppressed the migration, invasion capabilities, and proliferation of prostate cancer cells and induced cellular apoptosis. Furthermore, the invasion and migration of 22RV1 cells were mechanistically inhibited by SIRT4 via glutamine metabolism inhibition. In conclusion, the present study’s findings showed that SIRT4 protein levels are significantly associated with the Gleason score in patients with prostate cancer, and SIRT4 exerts a tumor-suppressive effect on prostate cancer cells by inhibiting glutamine metabolism. Thus, SIRT4 may serve as a potential novel therapeutic target for prostate cancer

The Hemagglutinin-Neuraminidase Protein of Newcastle Disease Virus Determines Tropism and Virulence

The hemagglutinin-neuraminidase (HN) protein of Newcastle disease virus (NDV) plays a crucial role in the process of infection. However, the exact contribution of the HN gene to NDV pathogenesis is not known. In this study, the role of the HN gene in NDV virulence was examined. By use of reverse genetics procedures, the HN genes of a virulent recombinant NDV strain, rBeaudette C (rBC), and an avirulent recombinant NDV strain, rLaSota, were exchanged. The hemadsorption and neuraminidase activities of the chimeric viruses showed significant differences from those of their parental strains, but heterotypic F and HN pairs were equally effective in fusion promotion. The tissue tropism of the viruses was shown to be dependent on the origin of the HN protein. The chimeric virus with the HN protein derived from the virulent virus exhibited a tissue predilection similar to that of the virulent virus, and vice versa. The chimeric viruses with reciprocal HN proteins either gained or lost virulence, as determined by a standard intracerebral pathogenicity index test of chickens and by the mean death time in chicken embryos (a measure devised to classify these viruses), indicating that virulence is a function of the amino acid differences in the HN protein. These results are consistent with the hypothesis that the virulence of NDV is multigenic and that the cleavability of F protein alone does not determine the virulence of a strain

Yield Prediction Models in Guangxi Sugarcane Planting Regions Based on Machine Learning Methods

ObjectiveAccurate prediction of changes in sugarcane yield in Guangxi can provide important reference for the formulation of relevant policies by the government and provide decision-making basis for farmers to guide sugarcane planting, thereby improving sugarcane yield and quality and promoting the development of the sugarcane industry. This research was conducted to provide scientific data support for sugar factories and related management departments, explore the relationship between sugarcane yield and meteorological factors in the main sugarcane producing areas of Guangxi Zhuang Autonomous Region.MethodsThe study area included five sugarcane planting regions which laid in five different counties in Guangxi, China. The average yields per hectare of each planting regions were provided by Guangxi Sugar Industry Group which controls the sugar refineries of each planting region. The daily meteorological data including 14 meteorological factors from 2002 to 2019 were acquired from National Data Center for Meteorological Sciences to analyze their influences placed on sugarcane yield. Since meteorological factors could pose different influences on sugarcane growth during different time spans, a new kind of factor which includes meteorological factors and time spans was defined, such as the average precipitation in August, the average temperature from February to April, etc. And then the inter-correlation of all the meteorological factors of different time spans and their correlations with yields were analyzed to screen out the key meteorological factors of sensitive time spans. After that, four algorithms of BP neural network (BPNN), support vector machine (SVM), random forest (RF), and long short-term memory (LSTM) were employed to establish sugarcane apparent yield prediction models for each planting region. Their corresponding reference models based on the annual meteorological factors were also built. Additionally, the meteorological yields of every planting region were extracted by HP filtering, and a general meteorological yield prediction model was built based on the data of all the five planting regions by using RF, SVM BPNN, and LSTM, respectively.Results and DiscussionsThe correlation analysis showed that different planting regions have different sensitive meteorological factors and key time spans. The highly representative meteorological factors mainly included sunshine hours, precipitation, and atmospheric pressure. According to the results of correlation analysis, in Region 1, the highest negative correlation coefficient with yield was observed at the sunshine hours during October and November, while the highest positive correlation coefficient was found at the minimum relative humidity in November. In Region 2, the maximum positive correlation coefficient with yield was observed at the average vapor pressure during February and March, whereas the maximum negative correlation coefficient was associated with the precipitation in August and September. In Region 3, the maximum positive correlation coefficient with yield was found at the 20‒20 precipitation during August and September, while the maximum negative correlation coefficient was related to sunshine hours in the same period. In Region 4, the maximum positive correlation coefficient with yield was observed at the 20‒20 precipitation from March to December, whereas the maximum negative correlation coefficient was associated with the highest atmospheric pressure from August to December. In Region 5, the maximum positive correlation coefficient with yield was found at the average vapor pressure from June and to August, whereas the maximum negative correlation coefficient as related to the lowest atmospheric pressure in February and March. For each specific planting region, the accuracy of apparent yield prediction model based on sensitive meteorological factors during key time spans was obviously better than that based on the annual average meteorological values. The LSTM model performed significantly better than the widely used classic BPNN, SVM, and RF models for both kinds of meteorological factors (under sensitive time spans or annually). The overall root mean square error (RMSE) and mean absolute percentage error (MAPE) of the LSTM model under key time spans were 10.34 t/ha and 6.85%, respectively, with a coefficient of determination Rv2 of 0.8489 between the predicted values and true values. For the general prediction models of the meteorological yield to multiple the sugarcane planting regions, the RF, SVM, and BPNN models achieved good results, and the best prediction performance went to BPNN model, with an RMSE of 0.98 t/ha, MAPE of 9.59%, and Rv2 of 0.965. The RMSE and MAPE of the LSTM model were 0.25 t/ha and 39.99%, respectively, and the Rv2 was 0.77.ConclusionsSensitive meteorological factors under key time spans were found to be more significantly correlated with the yields than the annual average meteorological factors. LSTM model shows better performances on apparent yield prediction for specific planting region than the classic BPNN, SVM, and RF models, but BPNN model showed better results than other models in predicting meteorological yield over multiple sugarcane planting regions