[Efficiency Characteristics and Evolution Patterns of Urban Carbon Metabolism of Production-Living-Ecological Space in Beijing-Tianjin-Hebei Region].

This study explored the carbon metabolism efficiency of a production-living-ecological space system, which is of great significance for regional factor integration and spatial optimization. In this study, the material flow analysis method was introduced to establish a framework for evaluating the carbon metabolism efficiency of the production-living-ecological space system, and the super-efficiency DEA model and Malmquist index were used to empirically analyze the spatio-temporal distribution, dynamic change, and evolution patterns of the carbon metabolism efficiency of production-living-ecological space in the Beijing-Tianjin-Hebei Region, China, from 2000 to 2020 on the basis of the urban metabolic perspective. The results showed that:① the carbon metabolism efficiency of the production-living-ecological space showed a fluctuating growth trend, indicating the significant spatial differentiation of carbon metabolism efficiency in each city. There was a low overall carbon metabolism efficiency level, with a distribution pattern of being high in the middle and low in the north and south. ② The Malmquist index showed that the Total Factor Productivity (TFP) of carbon metabolism efficiency was greater than 1, and both the Technical Change (TC) and Pure Efficiency Change (PEC) were less than 1, in which the TFP showed an increasing trend, whereas there was no significant contribution of technological progress or pure technical efficiency to carbon metabolism efficiency. The total factor productivity of more than 50% of the cities showed an improving trend, only 38.46% of which made technological progress in improving carbon metabolism efficiency, and more than half of the urban pure technical efficiency showed a decreasing trend, in which the technical efficiency change and scale efficiency change were greater than 1 in most cities. ③ There were different types of carbon efficiency characteristics in each city, and according to the movement rules of the corresponding points in the quartile map, the evolution patterns of tourism industry efficiency were classified into stable, reciprocating, progressive, and abrupt. Therefore, local governments should adopt differentiated strategies to reasonably allocate spatial resources of production-living-ecological space and improve the technical level and scale efficiency, so as to improve the efficiency of urban carbon metabolism.

Negative serum (1,3) -ß-D-glucan has a low power to exclude Pneumocystis jirovecii pneumonia (PJP) in HIV-uninfected patients with positive qPCR.

OBJECTIVE: The current study evaluated the diagnostic performance of serum (1,3)-beta-D Glucan (BDG) in differentiating PJP from P. jirovecii-colonization in HIV-uninfected patients with P. jirovecii PCR-positive results. METHODS: This was a single-center retrospective study between 2019 and 2021. The diagnosis of PJP was based on the following criteria: detection of P. jirovecii in sputum or BAL specimen by qPCR or microscopy; Meet at least two of the three criteria: (1) have respiratory symptoms of cough and/or dyspnea, hypoxia; (2) typical radiological picture findings; (3) receiving a complete PJP treatment. After exclusion, the participants were divided into derivation and validation cohorts. The derivation cohort defined the cut-off value of serum BDG. Then, it was verified using the validation cohort. RESULTS: Two hundred and thirteen HIV-uninfected patients were enrolled, with 159 PJP and 54 P. jirovecii-colonized patients. BDG had outstanding specificity, LR, and PPV for PJP in both the derivation (90.00%, 8.900, and 96.43%) and the validation (91.67%, 9.176, and 96.30%) cohorts at ≥ 117.7 pg/mL. However, it had lower sensitivity and NPV in the derivation cohort (89.01% and 72.97%), which was even lower in the validation cohort (76.47% and 57.89%). Of note, BDG ≥ 117.7 pg/mL has insufficient diagnostic efficacy for PJP in patients with lung cancer, interstitial lung disease (ILD) and nephrotic syndrome. And although lymphocytes, B cells, and CD4+ T cells in PJP patients were significantly lower than those in P. jirovecii-colonized patients, the number and proportion of peripheral blood lymphocytes did not affect the diagnostic efficacy of serum BDG. CONCLUSIONS: Serum BDG ≥ 117.7 pg/mL could effectively distinguish P. jirovecii-colonization from infection in qPCR-positive HIV-uninfected patients with infectious diseases, solid tumors (excluding lung cancer), autoimmune or inflammatory disorders, and hematological malignancies. Of note, for patients with lung cancer, ILD, and nephrotic diseases, PJP should be cautiously excluded at BDG < 117.7 pg/mL.

Identifying driving factors of urban land expansion using Google Earth Engine and machine-learning approaches in Mentougou District, China.

The research on driving mechanisms of urban land expansion is hot topic of land science. However, the relative importance of anthropogenic-natural factors and how they affect urban land expansion change are still unclear. Based on the Google Earth Engine platform, this study used the support vector machine classifier to extract land-use datasets of Mentougou district of Beijing, China from 1990 to 2016. Supported by machine-learning approaches, multiple linear regression (MLR) and random forests (RF) were applied and compared to identify the influential factors and their relative importance on urban land expansion. The results show: There was a continuous growth in urban land expansion from 1990 to 2016, the increased area reached 6097.42 ha with an average annual rate of 8.01% and average annual intensity rate of 2.57%, respectively. Factors such as elevation, risk of goaf collapse, accessibility, local fiscal expenditure, industrial restructuring, per capita income in rural area, GDP were important drivers of urban land expansion change. The model comparison indicated that RF had greater ability than MLR to identify the non-linear relationships between urban land expansion and explanatory variables. The influencing factors of urban land expansion should be comprehensively considered to regulate new land policy actions in Mentougou.

Metabolite and Transcriptome Profiling on Xanthine Alkaloids-Fed Tea Plant (Camellia sinensis) Shoot Tips and Roots Reveal the Complex Metabolic Network for Caffeine Biosynthesis and Degradation.

While caffeine is one of the most important bioactive metabolites for tea as the most consumed non-alcohol beverage, its biosynthesis and catabolism in tea plants are still not fully understood. Here, we integrated purine alkaloid profiling and transcriptome analysis on shoot tips and roots fed with caffeine, theophylline, or theobromine to gain further understanding of caffeine biosynthesis and degradation. Shoot tips and roots easily took up and accumulated high concentrations of alkaloids, but roots showed much faster caffeine and theophylline degradation rates than shoot tips, which only degraded theophylline significantly but almost did not degrade caffeine. Clearly feedback inhibition on caffeine synthesis or inter-conversion between caffeine, theophylline, and theobromine, and 3-methylxanthine had been observed in alkaloids-fed shoot tips and roots, and these were also evidenced by significant repression of TCS and MXMT genes critical for caffeine biosynthesis. Among these responsively repressed genes, two highly expressed genes TCS-4 and TCS-8 were characterized for their enzyme activity. While we failed to detect TCS-4 activity, TCS-8 displayed N-methyltransferase activities towards multiple substrates, supporting the complex metabolic network in caffeine biosynthesis in tea plants since at least 13 TCS-like N-methyltransferase genes may function redundantly. This study provides new insight into complex metabolic networks of purine alkaloids in tea plants.