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In addressing the challenges posed by the implications of climate change, understanding the phenological variations of woody plants has become a pivotal research topic. This research centers on the phenological shifts of woody plants and their connections with climatic factors in the southern and northern regions of the Nanling Mountains, which serve as the boundary between the north subtropical climate zone and the south subtropical climate zone in South China. The data were gathered through extensive manual observations conducted at four plant phenology observation stations (Ganxian, Foshan, Guilin, and Changsha) spanning different periods from 1963 to 2008. The study scrutinized four widely distributed woody plant species in the research area, specifically C. mollissima, P. fortunei, M. azedarach, and M. grandiflora. The analytical methods utilized were linear trend estimation and Pearson correlation coefficient analyses. The principal findings were as follows: (i) over the past several decades, the phenological stages of woody plants in the southern region consistently preceded those in the northern region with variations ranging from 2 to 38 days; (ii) an advancing trend of 0.1 to 2 days per decade was discerned in the phenological stages of all woody plants in the southern region; (iii) within the same geographic region, distinct species exhibited varying sensitivities to climatic factors, and M. azedarach demonstrated a particularly high sensitivity to climate fluctuations affecting phenological stages; and (iv) different climatic factors exerted distinct influences on individual plant species. Notably, temperature emerged as the primary driver of phenological changes, which was supported by a significant negative correlation between the phenological stages of the studied plants and spring temperatures. This study contributes to our understanding of the effects of climate change on plant phenology and offers valuable insights to guide ecological conservation and management strategies within the region. [ABSTRACT FROM AUTHOR]

Traditional Chinese medicines (TCMs) have been considered as important alternative therapeutics because of their significant medicinal benefits in specific diseases. Chinese herb formula is characterized by a vast molecule that differs in routine medicines. Due to TCMs chemical complexity, proper quality control has been a great challenge. Choosing the appropriate method to identify and qualify these compounds is an important work to ensure its safety, efficacy and quality control. Thus, this study aimed at providing novel information on high-resolution LTQ-Orbitrap mass spectrometer (UPLC-LTQ-Orbitrap-MSn) based identification of Bu Shen Yi Sui capsule (BSYSC), which is used in treating multiple sclerosis as a kind of TCMs. Under the proposed chromatographic conditions, 80 chemical components classified as anthraquinone, phenolic acid and phenylethanoid glycosides were separated and identified from BSYSC. Coupled with the high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS) method, eight of them were regarded as marker compounds for the quantitative evaluation of BSYSC. The identification and quantification with precision of UPLC-LTQ-Orbitrap-MSn and UPLC-QTOF-MS/MS could facilitate essential data for further pharmacokinetic studies of BSYSC. [ABSTRACT FROM AUTHOR]

The carbon emission intensity of the service industry is an important index to measure the harmonious relationship between the development of service industry and the ecological environment. First, the carbon emission intensity of China's service industry was calculated from 1995 to 2018 by means of the reference approach introduced by the IPCC. Then, spatial auto-correlation was used to analyze its spatial correlation feature. Finally, based on the EKC and the IPAT model, taking population density, economic development, technology, industrial structure and energy structure as explanatory variables, this study assessed the influencing factors of the carbon emission intensity of China's service industry by means of the GWR model. The results showed that: 1 The carbon emission intensity of China's service industry decreased from 1.43 tons per 10 000 yuan to 0.22 tons per 10 000 yuan during 1995-2018 with an average annual decrease of 3.53%. The period of high-speed descent was 1995 to 2000, and the period of slow descent was 2000 to 2018. The carbon emission intensity of China's service industry was high in the northwestern region while low in the southeastern region, and that in most provinces was below the national average. 2 There was significant spatial positive auto-correlation of carbon emission intensity of China's service industry. The spatial positive auto-correlation went through three stages: strengthening, weakening and strengthening again. The main spatial correlation types of carbon emission intensity of China's service industry were 'H-H' and 'L-L' agglomeration models. The spatial agglomeration of carbon emission intensity in China's service industry presented an obvious trend of optimization; the provinces with 'L-H' and 'H-L' agglomeration model decreased, while the ones with 'L-L' agglomeration model increased and spread from the eastern region to the central region. 3 Each influencing factor had significant influence on the carbon emission intensity of the service industry. Industrial structure and energy structure had positive effect on the carbon emission intensity of the service industry, while population density, economic development and technology had obvious negative effect on it and technology was the most important factor affecting the carbon emission intensity of the service industry. The degree of influence was shown in a descending order of technology, industrial structure, energy structure, economic development, and population density. Therefore, the central government and local governments should implement energy-saving and emission-reduction measures from the bottom up, establish and improve regional cooperation mechanisms, and improve the efficiency of resource allocation to achieve low-carbon development of the service industry. [ABSTRACT FROM AUTHOR]