An inventory of invasive alien species in China

Invasive alien species (IAS) are a major global challenge requiring urgent action, and the Strategic Plan for Biodiversity (2011–2020) of the Convention on Biological Diversity (CBD) includes a target on the issue. Meeting the target requires an understanding of invasion patterns. However, national or regional analyses of invasions are limited to developed countries. We identified 488 IAS in China’s terrestrial habitats, inland waters and marine ecosystems based on available literature and field work, including 171 animals, 265 plants, 26 fungi, 3 protists, 11 procaryots, and 12 viruses. Terrestrial plants account for 51.6% of the total number of IAS, and terrestrial invertebrates (104 species) for 21.3%. Of the total numbers, 67.9% of plant IAS and 34.8% of animal IAS were introduced intentionally. All other taxa were introduced unintentionally despite very few animal and plant species that invaded naturally. In terms of habitats, 64.3% of IAS occur on farmlands, 13.9% in forests, 8.4% in marine ecosystems, 7.3% in inland waters, and 6.1% in residential areas. Half of all IAS (51.1%) originate from North and South America, 18.3% from Europe, 17.3% from Asia not including China, 7.2% from Africa, 1.8% from Oceania, and the origin of the remaining 4.3% IAS is unknown. The distribution of IAS can be divided into three zones. Most IAS are distributed in coastal provinces and the Yunnan province; provinces in Middle China have fewer IAS, and most provinces in West China have the least number of IAS. Sites where IAS were first detected are mainly distributed in the coastal region, the Yunnan Province and the Xinjiang Uyghur Autonomous Region. The number of newly emerged IAS has been increasing since 1850. The cumulative number of firstly detected IAS grew exponentially

Interaction effects of polyfluoroalkyl substances and sex steroid hormones on asthma among children

To evaluate the interactions between polyfluoroalkyl substances (PFASs) and reproductive hormones and associated asthma, a total of 231 asthmatic and 225 non-asthmatic adolescents were selected from northern Taiwan in the Genetic and Biomarkers study for Childhood Asthma from 2009-2010. The interaction between PFASs and reproductive hormones on asthma was analyzed with a two-level binary logistic regression model. The results showed that, among asthmatics, PFASs were positively associated with estradiol levels and negatively associated with testosterone levels. However, only significant association was identified for PFNA and estradiol in control group. After controlling for hormone levels, associations between PFAS exposure and asthma were consistently stronger among children with higher than lower estradiol, with odds ratios (OR) for asthma ranging from 1.25 for PFOS (95% Confidence Interval [CI]: 0.90, 1.72) to 4.01 for PFDA (95% CI: 1.46, 11.06) among boys and 1.25 for PFOS (95% CI: 0.84, 1.86) to 4.16 for PFNA (95% CI: 1.36, 12.73) among girls. Notably, the interactions between estradiol and PFASs were significant for PFOS (p = 0.026) and PFNA (p = 0.043) among girls. However, testosterone significantly attenuated the association between PFOS and asthma across sex. In conclusions, our findings suggested that reproductive hormones amplify the association between PFASs and asthma among adolescents

Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal dependency features. Furthermore, a fully connected neural network is employed to achieve a regression model of RUL prognostics. Lastly, validated with the aid of numerical and graphic results by an equipment RUL dataset from the Commercial Modular Aero-Propulsion System Simulation(C-MAPSS), the proposed scheme turns out to be superior to four existing models regarding accuracy and effectiveness

Discussion on the Sustainable Development Pattern of Road Network Structure of Cities with a Population of 1-2 Million in China

The urban road network system is the main carrier of urban traffic. The constraint factor that urban road network has on traffic leads to major urban traffic problems. In this paper, a questionnaire survey and the Delphi method are determined to determine the case cities with a population of 1-2 million at home and abroad. Literature research and comparative, analytical, and inductive research methods are used to compare, analyse, and evaluate the advantages and disadvantages of the road network structure of the case cities, to summarize the development law of the urban road network, and to propose a sustainable development structure model for each development stage of urban road network: the mode made of ring freeway + trunk street (the embryonic stage), the mode made of ring freeway + expressway + trunk street (the incubation stage), the mode made of outer ring freeway + outer ring expressway and radial expressway + trunk street (the mature stage). The innovative point of this paper is to put forward a sustainable development pattern for the road network structure of cities with a population of 1-2 million in China

WaveletDFDS-Net: A Dual Forward Denoising Stream Network for Low-Dose CT Noise Reduction

The challenge of denoising low-dose computed tomography (CT) has garnered significant research interest due to the detrimental impact of noise on CT image quality, impeding diagnostic accuracy and image-guided therapies. This paper introduces an innovative approach termed the Wavelet Domain Dual Forward Denoising Stream Network (WaveletDFDS-Net) to address this challenge. This method ingeniously combines convolutional neural networks and Transformers to leverage their complementary capabilities in feature extraction. Additionally, it employs a wavelet transform for efficient image downsampling, thereby preserving critical information while reducing computational requirements. Moreover, we have formulated a distinctive dual-domain compound loss function that significantly enhances the restoration of intricate details. The performance of WaveletDFDS-Net is assessed by comparative experiments conducted on public CT datasets, and results demonstrate its enhanced denoising effect with an SSIM of 0.9269, PSNR of 38.1343 and RMSE of 0.0130, superior to existing methods

Recombinant ArgF PLGA nanoparticles enhances BCG induced immune responses against Mycobacterium bovis infection

Mycobacterium bovis (M. bovis) is a member of mycobacterium tuberculosis complex (MTBC), and a causative agent of chronic respiratory disease in a wide range of hosts. Bacillus Calmette-Guerin (BCG) vaccine is mostly used for the prevention of childhood tuberculosis. Further substantial implications are required for the development and evaluation of new tuberculosis (TB) vaccines as well as improving the role of BCG in TB control strategies. In this study, we prepared PLGA nanoparticles encapsulated with argF antigen (argF-NPs). We hypothesized, that argF nanoparticles mediate immune responses of BCG vaccine in mice models of M. bovis infection. We observed that mice vaccinated with argF-NPs exhibited a significant increase in secretory IFN-γ, CD4+ T cells response and mucosal secretory IgA against M. bovis infection. In addition, a marked increase was observed in the level of secretory IL-1β, TNF-α and IL-10 both in vitro and in vivo upon argF-NPs vaccination. Furthermore, argF-NPs vaccination resulted in a significant reduction in the inflammatory lesions in the lung’s tissues, minimized the losses in total body weight and reduced M. bovis burden in infected mice. Our results indicate that BCG prime-boost strategy might be a promising measure for the prevention against M. bovis infection by induction of CD4+ T cells responses and mucosal antibodies

A novel bivalent inactivated vaccine for ducks against Riemerella anatipestifer based on serotype distribution in southern China

ABSTRACT: Riemerella anatipestifer (RA) causes epizootic infectious polyserositis in ducks with high mortality and leads to huge economic losses worldwide. Bacterial resistance poses a challenge for the control of the disease, vaccines failed to provide ideal cross-protection. Thus, the preparation of vaccines based on popular serotypes is important. In this study, we collected 700 brain and liver tissues of dead ducks from 8 provinces in southern China from 2016 to 2022 and obtained 195 RA isolates with serotypes 1, 2, 7, and 10. Serotypes 1 and 2 were the most prevalent (82%). A novel bivalent inactivated vaccine WZX-XT5 containing propolis adjuvant was prepared, we chose XT5 (serotype 1) and WZX (serotype 2) as vaccine strains and evaluated WZX-XT5-induced immune response and protective efficacy in ducks. Results showed that the XT5 (LD50, 3.5 × 103 CFU) exhibited high virulence and provided better protection against RA compared with ZXP, DCR and LCF1 (LD50, 108 CFU). Notably, the dose of 109 CFU provided ideal protection compared with 108 CFU, propolis and oil emulsion adjuvants induced stronger protective efficacy compared with aluminum hydroxide adjuvant. Importantly, WZX-XT5 immunization induced high levels of RA-specific IgY, IFN-γ, IL-2, and IL-4 in serum and offered over 90% protection against RA with ultra-high lethal dose in ducks. Additionally, no clinical signs of RA infection or obvious pathological damage in tissues were observed in protected ducks. Overall, this study first reports the identification, serotyping and virulence of RA in ducks of southern China and the preparation of a novel bivalent inactivated vaccine, providing useful scientific information to prevent and control RA infection