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Abstract Background Acmella radicans (Jacquin) R.K. Jansen is a new invasive species record for Yunnan Province, China. Native to Central America, it has also been recently recorded invading other parts of Asia. To prevent this weed from becoming a serious issue, an assessment of its ecological impacts and potential distribution is needed. We predicted the potential distribution of A. radicans in China using the MaxEnt model and its ecological impacts on local plant communities and soil nutrients were explored. Results Simulated training using model parameters produced an area under curve value of 0.974, providing a high degree of confidence in model predictions. Environmental variables with the greatest predictive power were precipitation of wettest month, isothermality, topsoil TEB (total exchangeable bases), and precipitation seasonality, with a cumulative contribution of more than 72.70% and a cumulative permutation importance of more than 69.20%. The predicted potential suitable area of A. radicans in China is concentrated in the southern region. Projected areas of A. radicans ranked as high and moderately suitable comprised 5425 and 26,338 km2, accounting for 0.06 and 0.27% of the Chinese mainland area, respectively. Over the 5 years of monitoring, the population density of A. radicans increased while at the same time the population density and importance values of most other plant species declined markedly. Community species richness, diversity, and evenness values significantly declined. Soil organic matter, total N, total P, available N, and available P concentrations decreased significantly with increasing plant cover of A. radicans, whereas pH, total K and available K increased. Conclusion Our study was the first to show that A. radicans is predicted to expand its range in China and may profoundly affect plant communities, species diversity, and the soil environment. Early warning and monitoring of A. radicans must be pursued with greater vigilance in southern China to prevent its further spread.

The fall armyworm (FAW) is an invasive pest that has been rapidly spreading across China since its detection in Yunnan province in January 2019. Although sugarcane and sorghum have been reported as hosts, their effects on FAW’s population growth and life table parameters have not been examined in China. Our research shows that FAW’s development and life table metrics vary significantly when reared on sorghum, sugarcane, and maize. Notably, the preadult stage, adult preoviposition period, and total preoviposition period of FAW were markedly longer on sugarcane and sorghum compared to maize. FAW reared on these two crops also exhibited reduced survival rates, pupal weight, fecundity, and lower female-to-male ratios. The study highlights that FAW had lower population growth rates, reproductive rates, and longer generation times on sugarcane and sorghum compared to maize. The consumption index and digestibility index were higher on maize, while conversion efficiency and growth rate were greater on sorghum. Although maize is the most favorable host, FAW can still survive and reproduce on sugarcane and sorghum during the nongrowing season, posing a risk to economically important crops in China. Despite being less favorable for population growth, sugarcane and sorghum still support FAW development and spread. Therefore, enhanced surveillance and early warning measures for sugarcane and sorghum are recommended to monitor FAW population dynamics and mitigate its potential impact on primary host plants.

Abstract The tomato pinworm, Tuta absoluta, or Phthorimaea absouta, is native to South America, but quickly spread to other regions of world, including Europe, Africa, and Asia, devastating to global tomato production. However, a lack of high-quality genome resources makes it difficult to understand its high invasiveness and ecological adaptation. Here, we sequenced the genome of the tomato pinworm using Nanopore platforms, yielding a genome assembly of 564.5 Mb with contig N50 of 3.33 Mb. BUSCO analysis demonstrated that this genome assembly has a high-level completeness of 98.0% gene coverage. In total, 310 Mb are repeating sequences accounting for 54.8% of genome assembly, and 21,979 protein-coding genes are annotated. Next, we used the Hi-C technique to anchor 295 contigs to 29 chromosomes, yielding a chromosome-level genome assembly with a scaffold N50 of 20.7 Mb. In sum, the high-quality genome assembly of the tomato pinworm is a useful gene resource that contributes to a better understanding of the biological characteristics of its invasiveness and will help in developing an efficient control policy.

The red imported fire ants (RIFA, Solenopsis invicta) have become a well-known invasive species that poses significant ecological and economic threats globally. As of recent times, the geographic scope of its invasion in China is rapidly expanding, thereby aggravating the extent and severity of its detrimental effects. The importance of soil microorganisms for maintaining soil health and ecosystem function has been widely acknowledged. However, the negative impact of RIFAs on soil microbial communities and their functions has not yet been fully understood. In this study, we sequenced the V3-V4 variable region of the bacterial 16S rRNA gene in soil samples collected from three types of RIFA nests to investigate the impact of RIFA invasion on soil microbial diversity and composition. The results of alpha diversity analysis showed that the normal soil without nests of RIFAs exhibited the highest level of diversity, followed by the soil samples from RIFA-invaded nests and abandoned nests. Taxonomy and biological function annotation analyses revealed significant differences in microbial community structure and function among the different samples. Our findings demonstrate that RIFA invasion can significantly alter soil microbial community composition, which could ultimately affect ecosystem function. Therefore, effective management strategies are urgently needed to mitigate the negative impact of invasive species on native ecosystems.

IntroductionIn natural systems, diverse plant communities tend to prevent a single species from dominating. Similarly, management of invasive alien plants may be achieved through various combinations of competing species.MethodsWe used a de Wit replacement series to compare different combinations of sweet potato (Ipomoea batatas (L.) Lam), hyacinth bean (Lablab purpureus (L.) Sweet) and mile-a-minute (Mikania micrantha Kunth) through measures of photosynthesis, plant growth, nutrient levels in plant tissue and soil, and competitive ability.ResultsCultured alone sweet potato and hyacinth beans exhibited higher total biomass, leafstalk length, and leaf area than mile-a-minute. In mixed culture, either sweet potato or hyacinth bean or both together significantly suppressed the mile-a-minute parameters, i.e., plant height, branch, leaf, adventitious root, and biomass (P

Abstract Flood control initiated Chinese civilization. Chinese civilization has a long history, and Chinese ancestors invented “aquatic engineering technology” for flood control and water management, including the Fascine body, as early as two thousand years ago. The Fascine body is a structure used to protect the bank and prevent breaching of an ancient Chinese flood control project. Excavation of the Shahe ancient bridge ruins in Xi’an City, Shaanxi Province, China led to discovery of a Fascine body structure. Through 14C dating, fibre, slice observations, infrared spectroscopy, X-ray diffraction, thermogravimetric analyses and SEM energy spectrum analyses of the fascine material, microbes in the structure and the surrounding soil were identified, the dominant microbial community was analyzed, and microbial diseases were controlled in a targeted manner. Research on the fascine bank ruins identified the width and length boundaries of the Shahe ancient bridge, determined the exact age of the Shahe ancient bridge, provided important materials for research on ancient bridges, river embankments and other ruins, and provided important clues for the traffic and layout around Chang’an during the Qin and Han Dynasties of Chinese history.

Green and low-carbon has become the main theme of global energy development. Data centers are the core of the digital age, carrying huge arithmetic demand. Data centers must implement green low-carbon energy efficiency management to improve energy efficiency, reduce energy waste and carbon emissions, and achieve sustainable development. As a result, an intelligent management strategy for dynamic energy efficiency of data center networks with Artificial Intelligence (AI) fitting control is proposed. Firstly, a Long Short-Term Memory (LSTM) network is used for long sequence trend prediction to predict the temperature of the data center in the next sequence using the temperature of the past 15 sequences and the power consumption of the equipment as parameters. Then, based on the prediction results, the intelligent air conditioning controller based on Deep Q-Network (DQN) is designed to update the parameters by using the gradient of double-Q network and error backpropagation, and the optimal control action is selected by using the ε-greedy strategy to ensure that the prediction of the hotspot does not occur. Experiments show that the average absolute errors of temperature prediction for supply air, return air, cold aisle as well as hot aisle are 0.32 °C, 0.21 °C, 0.36 °C and 0.19 °C, respectively. The Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE) decreased by an average of 2.6% and 2.5%, respectively. The method achieves the purpose of predicting future temperatures and intelligently controlling the output so that the data center can satisfy the premise of normal operation and thus achieve more efficient energy use.

Purpose: The hyper-proliferation, promoted migration, fibrosis, and calcification of pulmonary arterial smooth muscle cells (PASMCs) play critical roles in pulmonary artery (PA) continuous contraction and vascular remodeling, leading to elevated pulmonary arterial resistance and pulmonary hypertension (PH). In this study, we sought to ascertain the effects of a TOR2A gene product, salusin-β, on PASMCs’ proliferation, migration, fibrosis, calcification, and the imbalance of vasomotor function as well as pulmonary vascular remodeling in monocrotaline (MCT)-induced PH rats and their underlying mechanisms.Methods: Knockdown or overexpression of salusin-β in rats or PASMCs was performed through tail vein injection or cell transfection of virus. The right ventricular systolic pressure (RVSP) of the rat was measured by right ventricle catheterization. Sodium nitroprusside (SNP) or acetylcholine (ACh)-induced dose-dependent relaxation was used to evaluate the vasodilatation function. Primary PASMCs were isolated from the PAs of control and PH rats.Results: The salusin-β protein expressions were significantly increased in PAs and PASMCs isolated from PH rats compared with control rats. Knockdown of salusin-β in rats decreased high K+ solution-induced contraction, RVSP and RV hypertrophy index, improved SNP or ACh-induced vascular relaxation of PAs, and relieved vascular remodeling and calcification of PAs from PH rats. Silencing salusin-β in PASMCs isolated from PH rats alleviated the proliferation, migration, fibrosis, and calcification, as well as the NAD(P)H oxidase activity and reactive oxygen species (ROS) level. Overexpression of salusin-β exerted the opposite effects on vasomotor function and vascular remodeling, and PASMCs proliferation, migration, fibrosis and calcification.Conclusion: Increased salusin-β activity in PAs from PH rats contributes to PASMCs proliferation, migration, fibrosis, and calcification, leading to the imbalance of vascular contraction and relaxation and vascular remodeling through stimulating the production of NAD(P)H oxidase derived ROS.

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Sweet potato [Ipomoea batatas (L.) Lam] is grown as important cash and food crop worldwide and has been shown to exhibit allelopathic effects on other plants. However, its metabolome has not been studied extensively, particularly with respect to the production of phytotoxic bioactive secondary products. In this study, the chemical composition of petroleum ether extract of sweet potato was characterized, and the morphological and physiological effects of some individual components against four invasive alien weeds Bidens pilosa L., Galinsoga parviflora Cav., Lolium multiflorum Lam., and Phalaris minor Retz. were determined. Twenty-one components were identified by GS-MS, constituting 96.08% of petroleum ether extract in sweet potato. The major components were palmitic acid (PA) (17.48%), ethyl linoleate (EL) (13.19%), linoleic acid (LA) (12.55%), ethyl palmitate (EP) (11.77%), ethyl linolenate (ELL) (8.29%) oleic acid (5.82%), ethyl stearate (4.19%), and 3-methylphenol acetate (3.19%). The five most abundant compounds exhibited strong inhibition activity against the four invasive weeds tested. The highest inhibition rates were seen for LA, followed by PA and EP, respectively. Catalase (CAT), malondialdehyde (MDA), and peroxidase (POD) content of L. multiflorum were increased by the three allelochemicals, i.e., LA, PA and EP, but superoxide dismutase (SOD), chlorophyll-a and chlorophyll-b levels declined. Overall, the combined impact of all five compounds could be quite effective in suppressing the invasive weeds of concern.

With the proposed goal of “Carbon Neutrality”, photovoltaic energy is gradually gaining the leading role in energy transformation. At present, crystalline silicon cells are still the mainstream technology in the photovoltaic industry, but due to the similarity of defect characteristics and the small scale of the defects, automatic defect detection of photovoltaic cells (PV) by electroluminescence (EL) imaging is a challenging task. In order to better meet the growing demand for high-quality photovoltaic cell products in intelligent manufacturing and use, and ensure the safe and efficient operation of photovoltaic power stations, this paper proposes an improved abnormal detection method based on Faster R-CNN for the surface defect EL imaging of photovoltaic cells, which integrates a lightweight channel and spatial convolution attention module. It can analyze the crack defects in complex scenes more efficiently. The clustering algorithm was used to obtain a more targeted anchor frame for photovoltaic cells, which made the model converge faster and enhanced the detection ability. The normalized distance between the prediction box and the target box is minimized by considering the DIoU loss function for the overlapping area of the boundary box and the distance between the center points. The experiment shows that the average accuracy of surface defect detection for EL images of photovoltaic cells is improved by 14.87% compared with the original algorithm, which significantly improves the accuracy of defect detection. The model can better detect small target defects, meet the requirements of surface defect detection of photovoltaic cells, and proves that it has good application prospects in the field of photovoltaic cell defect detection.

The fall armyworm, Spodoptera frugiperda is a major agricultural pest in China, and has migrated from its continuous breeding area to other parts of China. In our study, the biological behaviors of S. frugiperda fed on maize, wheat, barley, faba beans, and soya beans were evaluated in a growth chamber. Results indicated that maize-fed S. frugiperda larvae performed well, as evidenced by shorter larva-adult periods, adult pre-oviposition period (APOP), total pre-oviposition period (TPOP), and generation time (T), and a higher survival rate, intrinsic (r) and finite (λ) rate of increase, and net reproductive rate (Ro), However, S. frugiperda larvae performed weakly when fed barley and faba bean plants, as indicated by lower survival rates, r, and λ, and longer pre-adult period, TPOP, and T. A heavier pupal weight of both sexes was recorded on faba beans (0.202 g) and a lighter weight on barley (0.169 g). Fecundity was higher when fed faba beans and maize, and lower when fed wheat and barley. Thus, maize was the most optimal and barley was the least optimal host plant, followed by faba beans, for S. frugiperda larvae growth and development. This study enhances our knowledge of S. frugiperda in these host plants and can help in the design of management approaches.