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In recent years, the invasion of Phyllostachys edulis has intensified. This study selected Phyllostachys edulis, Phyllostachys edulis–Quercus acutissima mixed and Quercus acutissima forest areas and analyzed the changes in soil bulk density, porosity, water-holding capacity, pH, soil organic carbon (SOC), soil readily oxidized organic carbon (ROC), soluble organic carbon (DOC) and microbial biomass carbon (SMBC). Then, we obtained the Chao index, Shannon index and Simpson index and analyzed the relationship between microbial α diversity, β diversity and community composition, abundance and structure and between microbial community changes and environmental factors. The results showed that soil with the invasion of Phyllostachys edulis, soil pH, water content, capillary water capacity and total porosity of surface soil increased significantly by 4.35%, 18.71%, 16.37% and 14.56%, respectively, compared with the Quercus acutissima forest, while soil bulk density, organic carbon, microbial biomass carbon and soluble organic carbon decreased significantly by 7.27%, 18.43%, 61.12% and 61.90%. Soil readily oxidized organic carbon decreased at first and then increased. The soil community richness and diversity of the Phyllostachys edulis forest were significantly greater than those of the other two stands. Soil pH and organic carbon content were the main factors affecting the changes in the soil microbial community. Therefore, the invasion of Phyllostachys edulis improved soil water retention, while soil pH had the greatest effect on the microbial community, followed by soil bulk density and organic carbon, and water content had the smallest effect.

The family of phosphatidylethanolamine-binding proteins (PEBPs) participates in various plant biological processes, mainly flowering regulation and seed germination. In cucurbit crops, several PEBP genes have been recognized to be responsible for flowering time. However, the investigation of PEBP family members across the genomes of cucurbit species has not been reported, and their conservation and divergence in structure and function remain largely unclear. Herein, PEBP genes were identified from seven cucurbit crops and were used to perform a comparative genomics analysis. The cucurbit PEBP proteins could be classified into MFT, FT, TFL, and PEBP clades, and further, the TFL clade was divided into BFT-like, CEN-like, and TFL1-like subclades. The MFT-like, FT-like, and TFL-like proteins were clearly distinguished by a critical amino acid residue at the 85th position of the Arabidopsis FT protein. In gene expression analysis, CsaPEBP1 was highly expressed in flowers, and its expression levels in females and males were 70.5 and 89.2 times higher, respectively, than those in leaves. CsaPEBP5, CsaPEBP6, and CsaPEBP7 were specifically expressed in male flowers, with expression levels 58.1, 17.3, and 15.7 times higher, respectively, than those of leaves. At least five CsaPEBP genes exhibited the highest expression during the later stages of corolla opening. Through clustering of time-series-based RNA-seq data, several potential transcription factors (TFs) interacting with four CsaPEBPs were identified during cucumber corolla opening. Because of the tandem repeats of binding sites in promoters, NF-YB (Csa4G037610) and GATA (Csa7G64580) TFs appeared to be better able to regulate the CsaPEBP2 and CsaPEBP5 genes, respectively. This study would provide helpful information for further investigating the roles of PEBP genes and their interacting TFs in growth and development processes, such as flowering time regulation in cucurbit crops.

Climate change mitigation and carbon neutrality are current hot topics. Forests, urban green spaces, and wetland ecosystems are recognized as important carbon sinks. The Yangtze River Delta region in Eastern China, which plays a pivotal role in China’s economic and social development, is rich in such carbon-sink resources. There is, however, a lack of regional carbon data. The investigation of carbon storage and carbon densities of forest, urban green space, and wetland ecosystems is, therefore, of great importance. In this study, the forest resource management map (including wetland) and green space system planning map of Changzhou city, combined with a field investigation and laboratory experimental analysis, were used to estimate the carbon storages and carbon densities of the forest, urban green space, and wetland ecosystems in Changzhou city. The average carbon density and carbon storage in Changzhou were 83.34 ± 4.91 Mg C ha−1 and 11.30 ± 0.67 Tg C, respectively, of which soil accounted for 74%, plants accounted for 25%, and litter accounted for less than 1%. The forest ecosystem contributed the most to the carbon pool (72%), with the green space ecosystem and the wetland ecosystem each accounting for 14% of the carbon pools. Clearly, the forest, green space, and wetland ecosystems in Changzhou city have a large carbon storage capacity. This study is of significance as it provides data on the carbon sink functions of forest, green space, and wetland ecosystems at the provincial and national regional scales.

It is important for food recognition to separate each ingredient within a food image at the pixel level. Most existing research has trained a segmentation network on datasets with pixel-level annotations to achieve food ingredient segmentation. However, preparing such datasets is exceedingly hard and time-consuming. In this paper, we propose a new framework for ingredient segmentation utilizing feature maps of the CNN-based Single-Ingredient Classification Model that is trained on the dataset with image-level annotation. To train this model, we first introduce a standardized biological-based hierarchical ingredient structure and construct a single-ingredient image dataset based on this structure. Then, we build a single-ingredient classification model on this dataset as the backbone of the proposed framework. In this framework, we extract feature maps from the single-ingredient classification model and propose two methods for processing these feature maps for segmenting ingredients in the food images. We introduce five evaluation metrics (IoU, Dice, Purity, Entirety, and Loss of GTs) to assess the performance of ingredient segmentation in terms of ingredient classification. Extensive experiments demonstrate the effectiveness of the proposed method, achieving a mIoU of 0.65, mDice of 0.77, mPurity of 0.83, mEntirety of 0.80, and mLoGTs of 0.06 for the optimal model on the FoodSeg103 dataset. We believe that our approach lays the foundation for subsequent ingredient recognition.

Conjugated diyne derivatives are important scaffolds in modern organic synthetic chemistry. Using the Glaser reaction involves the coupling of terminal alkynes which can efficiently produce conjugated diyne derivatives, while the use of a stoichiometric amount of copper salts, strong inorganic base, and excess oxidants is generally needed. Developing an environmentally friendly and effective method for the construction of symmetrical 1,3-diynes compounds by Glaser coupling is still highly desirable. In this study, we present an economical method for the production of symmetric diynes starting from various terminal acetylenes in a Glaser reaction. A simple and practical bis-N-heterocyclic carbene ligand has been introduced as efficient ligands for the Cu-catalyzed Glaser reaction. High product yields were obtained at 100 °C for a variety of substrates including aliphatic and aromatic terminal alkynes and differently substituted terminal alkynes including the highly sterically hindered substrate 2-methoxy ethynylbenzene or 2-trifluoromethyl ethynylbenzene and a series of functional groups, such as trifluoromethyl group, ester group, carboxyl group, and nitrile group. The established protocol is carried out in air under base-free condition and is operationally simple. These research work suggest that bis-N-heterocyclic carbene could also an appealing ligand for Glaser reaction and provide a reference for the preparation of symmetric 1,3-diynes in industrial filed.

With the comprehensive emissions of fossil fuel combustion and transportation waste gas, the concentrations of nitrogen oxides (NOX) in the environmental atmosphere increase significantly, leading to nitric acid rain (NAR) pollution. However, the effects of NAR on soil enzyme activities and soil microbial metabolism are unclear. In this study, the Quercus acutissima Carruth. forest in the Yangtze River Delta of China was selected as the experimental subject, and was exposed to the simulated spraying of NAR with pH values of 2.5, 3.5, and 4.5 to study the response of the forest soil enzyme activities and soil microbial metabolism to NAR. The results showed that compared to the non-NAR treatment, the activities of β-1,4-glucosidase (BG), L-leucine aminopeptidase (LAP), and β-1,4-N-acetylglucosidase (NAG) decreased by 56.48%–42.24%, 44.57%–38.20%, and 56.13%–48.11% under the AR2.5 and AR3.5 treatments, respectively. Moreover, there was no significant change in the Vector Length (VL) under different gradients of NAR. The Vector Angle (VA) increased with the decrease of the pH value and reached the maximum value with the AR2.5 treatment, indicating that the strong acid type NAR had a greater phosphorus-limiting effect on the soil microorganisms. The RDA analysis results showed that the dissolved organic carbon (DOC) was a significant factor affecting the soil enzyme activity and stoichiometric ratio, with interpretation rates of 40.2%. In conclusion, we believe that in the restoration of acidified soil, attention should be paid to the regulation of soil pH, reducing scour.

Soil labile organic carbon sensitively reflects subtle changes in the soil carbon pool and is an important aspect of forest soil carbon pool research. However, little is known regarding soil labile organic carbon storage and its dynamic changes during the development of Quercus acutissima Carruth. forests. Consequently, we investigated the dynamic changes in soil organic carbon and its labile organic carbon fraction stocks at soil depths of 0–10 cm, 10–20 cm, and 20–40 cm along a 17-year-old, 26-year-old, and 65-year-old chronosequence in Quercus acutissima forests. We found that stand age significantly impacted particulate organic carbon (POC), light fraction organic carbon (LFOC), and soil organic carbon (SOC). The POC, LFOC, and SOC contents at different soil depths exhibited an increasing trend with stand age, which could be described by simple linear regression. However, there was no noteworthy difference in the soil water-soluble organic carbon (WSOC) content between different stand ages. Moreover, the 17-year-old, stand had higher POC, LFOC, and WSOC to SOC ratios. Soil nutrients significantly affected organic carbon and fractions, which revealed that POC, LFOC, WSOC, and SOC were remarkably positively correlated with alkaline hydrolysis nitrogen (AN) and available phosphorus (AP) (p < 0.05). Furthermore, WSOC, POC, LFOC, and SOC were significantly positively correlated with available potassium (AK) (p < 0.05). POC, LFOC, and SOC storage in the 0–40 cm soil layer increased with stand development, while WSOC storage decreased at 65a. In addition, LFOC stocks accounted for the highest proportion of organic carbon stocks. Our results indicated that the development of Quercus acutissima forests was a process of carbon sink; however, the soil organic carbon activity was high, and the soil structure was unstable during the early development stage.

Nitric acid rain (NAR) seriously affects the biogeochemical cycles of forest communities’ ecosystems. However, the effects of NAR on the composition and diversity of the soil bacterial community remain unclear. In this study, a typical subtropical forest of Quercus acutissima was selected and simulated spraying of NAR at pH 2.5 (AR2.5), 3.5 (AR3.5), and 4.5 (AR4.5) was implemented to investigate the response of the forest soil bacterial communities to NAR. The results showed that the total number of OTUs of soil bacteria in AR2.5 and AR3.5 treatments was 1.11 and 1.23 times that in the control treatment without NAR (CK), respectively. Acidobacteria, Proteobacteria, and Actinobacteria were the dominant phyla in the subtropical forest, accounting for more than 80% of the community’s relative abundance. Concurrently, simulated NAR changed the relative abundance of Rhodanobacter significantly, which could be an indicator of soil bacterial community structure under NAR stress. Moreover, the Chao1, Shannon, and Simpson indices of strong acid rain treatments (i.e., AR2.5 and AR3.5) increased by 9.55%–22.5%, 3.6%–7.43%, and 0.15%–0.26%, respectively, compared to CK. Redundancy and correlation analysis illustrated that the phylum level structure of the bacterial community was significantly affected by soil total carbon, total nitrogen, and ammonium nitrogen. Our findings contribute to a better understanding of the effects of NAR on soil microbial communities and potential soil element cycling in north subtropical forests.

As a major human activity, urbanization exerts a strong impact on the fragile ecosystem in the Hindu Kush Himalayas (HKH) region. To maintain sustainable development, reliable data on urban land change are required to assess the impact of urbanization. Here, the reliability evaluation of four global fine-resolution impervious surface area (ISA) products: global annual impervious area (GAIA), global annual urban dynamics (GAUD), global impervious surface area (GISA), and global urban expansion (GUE) was carried out. The characteristics of urban expansion for five representative cities including Kabul, Lhasa, Lijiang, Thimphu, and Xining were remarkably different. Based on the results of incremental analysis and the spatial difference of the ISA, it was found that the GAIA dataset at a 30-m spatial resolution could provide better ISA information than the others in characterizing urban expansion in the mountainous region. Subsequently, the changes in the urban area were analyzed using the GAIA dataset from 1993 to 2018. In general, human settlements had grown, with the transformation of small villages into larger towns and some towns into major cities. Urban expansion would continuously intensify the contradictions between human activity and sustainability and exert a more significant impact on the fragile ecosystem in the HKH region. More attention should be paid to the impact of urbanization on the fragile mountainous ecosystem.

In this paper, the dual hesitant fuzzy rough set (DHFRS) is studied from the viewpoint of assessment deviations. Firstly, according to the relationship between intuitionistic fuzzy set and vague set, the DHFRS is transferred into a fuzzy set, where the membership of any given element to it has multi-grouped values. By the idea of bootstrap sampling, a group of four sets are generated to describe the membership degree on DHFRS, where the elements of the aforementioned sets are all considered as assessment values. Secondly, the generated sets are dealt with by assessment deviation theories, and specifically, two variables are proposed to describe the systematic and random deviations of the sets. Thirdly, the true-value of the membership degree of any elements to the set is estimated by a deviation-based dual hesitant fuzzy rough weighted aggregating operator. Fourthly, a dual hesitant fuzzy rough pattern recognition approach based on assessment deviation theories is proposed. Finally, an urban traffic modes recognition example is given to illustrate the validity of the proposed theories on DHFRSs.