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Winter wheat is a significant global food crop, and it is crucial to monitor its distribution for better agricultural management, land planning, and environmental sustainability. However, the distribution style of winter wheat planting fields is not consistent due to different terrain conditions. In mountainous areas, winter wheat planting units are smaller in size and fragmented in distribution compared to plain areas. Unfortunately, most crop-mapping research based on deep learning ignores the impact of topographic relief on crop distribution and struggles to handle hilly areas effectively. In this paper, we propose a cross-modal segmentation network for winter wheat mapping in complex terrain using remote-sensing multi-temporal images and DEM data. First, we propose a diverse receptive fusion (DRF) module, which applies a deformable receptive field to optical images during the feature fusion process, allowing it to match winter wheat plots of varying scales and a fixed receptive field to the DEM to extract evaluation features at a consistent scale. Second, we developed a distributed weight attention (DWA) module, which can enhance the feature intensity of winter wheat, thereby reducing the omission rate of planting areas, especially for the small-sized regions in hilly terrain. Furthermore, to demonstrate the performance of our model, we conducted extensive experiments and ablation studies on a large-scale dataset in Lanling county, Shandong province, China. Our results show that our proposed CM-Net is effective in mapping winter wheat in complex terrain. [ABSTRACT FROM AUTHOR]

As one of the most polluted provinces in China, air pollution events occur frequently in Shandong. Based on the hourly (or daily) concentrations of six air pollutants (PM2.5, PM10, O3, NO2, SO2 and CO), the situations of air quality improvement in three kinds of cities (key cities, coastal cities and general cities) are assessed comprehensively during 2014–2020. Contrary to the daily maximum 8-h average ozone (MDA8 O3), the annual average concentrations of other pollutants show the downward trends during 2014–2020. Therein, the improvement rates of annual average concentrations of air pollutants in key cities are highest. By 2020, the day proportions of O3 as the primary pollutant are up to 38% in three kinds of cities. Besides, due to the impact of COVID-19, the monthly average concentrations of PM2.5, PM10, NO2, SO2 and CO in February 2020 decrease by 32.1–49.5% year-on-year. There are still about 50% of population exposed to high-risk regions (Ri > 2), which are mainly concentrated in main urban areas and industrial areas. Thus, the adjustment of industrial structure and energy composition in the context of carbon peak and carbon neutrality should be implemented in the future. [ABSTRACT FROM AUTHOR]

Phylloporia, a wood-inhabiting fungal genus, has been used as an anti-inflammatory food taken in the form of tea in Shandong Province, China. The species used in folklore have been clarified as Phylloporia fontanesiae and P. lonicerae. To promote the utilization of Phylloporia and its benefit to human health, sources, medicinal metabolites and functions, safety, and commercial potentials are summarized in this review. The fruiting bodies are the most acceptable sources of Phylloporia, but their shortage makes fermented mycelia a good alternative. Polysaccharides, sterols, acidic components, styryl compounds and polychlorinated compounds are the most well-studied medicinal metabolites extracted from Phylloporia. The main medicinal functions revealed from Phylloporia include antitumor, immunomodulation, antioxidant, neuroprotection, prevention of diabetes and its complications, and antimicrobial activity. The safety of Phylloporia has been demonstrated by experimenting for acute and subchronic oral toxicity of fermented mycelia on animals. A large number of patents even if some are being examined have indicated the commercial potentials of Phylloporia. In the future, increased efforts regarding species identification, artificial cultivation and multi-omics data will contribute to the deeper exploration of the medicinal potential of Phylloporia. [ABSTRACT FROM AUTHOR]

Abnormal climate changes have resulted in over-precipitation in many regions. The occurrence and contamination levels of mycotoxins in crops and cereals have been elevated largely. From 2017 to 2019, we did investigation targeting 15 mycotoxins shown in the wheat samples collected from Shandong, a region suffering over-precipitation in China. We found that deoxynivalenol (DON) was the dominant mycotoxin contaminating wheats, with detection rates 304/340 in 2017 (89.41%), 303/330 in 2018 (91.82%), and 303/340 in 2019 (89.12%). The ranges of DON levels were < 4 to 580 μg/kg in 2017, < 4 to 3070 μg/kg in 2018, and < 4 to 1540 μg/kg in 2019. The exposure levels were highly correlated with local precipitation. Male exposure levels were generally higher than female's, with significant difference found in 2017 (1.89-fold, p = 0.023). Rural exposure levels were higher than that of cities but not statistically significant (1.41-fold, p = 0.13). Estimated daily intake (EDI) and margin of exposure (MoE) approaches revealed that 8 prefecture cities have probabilistically extra adverse health effects (vomiting or diarrhea) cases > 100 patients in 100,000 residents attributable to DON exposure. As a prominent wheat-growing area, Dezhou city reached ~ 300/100,000 extra cases while being considered as a major regional contributor to DON contamination. Our study suggests that more effort should be given to the prevention and control of DON contamination in major wheat-growing areas, particularly during heavy precipitation year. The mechanistic association between DON and chronic intestinal disorder/diseases should be further investigated. [ABSTRACT FROM AUTHOR]

We investigated the distribution of 8 per- and polyfluoroalkyl substances (PFASs) in 321 shellfish samples collected from four coastal cities along the Yellow Sea and Bohai Sea of Shandong Province, China. The concentrations of total PFASs (∑PFASs) were in the range of 0.061–178.259 ng/g wet weight (ww). Two legacy long-chain compounds were dominant. Three emerging compounds and two short-chain alternatives were also observed with higher concentration and detection frequency than in previous studies, whereas 4, 8-dioxa-3H-perfluorononanoate acid (ADONA) was not detected. There were differences in concentrations and composition profiles of PFASs among different species, as well as among different sampling cities. According to scores of principal component analysis, metal plating plants, textile treatments, and fluoropolymer products were considered as the main sources of PFAS contamination in shellfish. Furthermore, a potential health risk of perfluorooctanoic acid should be highly considered for local residents that frequently consume crabs and molluscs. • Legacy PFASs were dominant in shellfish from the Yellow Sea and Bohai Sea. • PFASs alternatives were detected more frequently, indicating an increase in use. • Industrial pollution was the main potential source of PFASs in studied shellfish. • PFOA showed a potential risk via consuming seafood to local residents in Shandong. [ABSTRACT FROM AUTHOR]

Duck salmonellosis is a common acute septic infectious disease that spreads rapidly, with serious harm to the duck breeding industry and public health. To date, there are few reports about the epidemiological characteristics of drug resistance in Salmonella from ducks. In this study, an epidemiological investigation was conducted on drug resistance of 110 Salmonella strains isolated from multiple duck farms in Shandong Province and surrounding areas, China. The multidrug-resistant (MDR) rate for 110 Salmonella strains was up to 71.82% (79/110), and 12 types of drug resistance genes were detected in all isolates, including β-lactams, aminoglycosides, tetracycline, macrolides, and quinolones resistance genes. Using the multilocus sequence typing (MLST) based on 7 housekeeping genes, 13 various ST types were identified among all strains, and ST19 (32/110, 29.09%) was the primary type. As the dominant serotypes, S. Kottbus and S. Typhimurium were divided into multiple ST types. A total of 6 kinds of plasmid incompatibility groups were carried in the Salmonella strains, of which IncFIIs (29/110, 26.36%) was most prevalent, and the class I integrons were detected in 78.18% (86/110) of strains. Furthermore, we found that some drug resistance genes, plasmid incompatibility groups, and class I integrons coexist in the same strain. This phenomenon indicates that class I integrons and plasmids are important ways for the spread of drug resistance genes. Therefore, the spread of antibiotic resistance in Salmonella had been facilitated, especially erythromycin (108/110, 98.18%), streptomycin (93/110, 84.54%), and tetracycline (53/110, 48.18%). The above research results broadened ideas and provided directions for the transmission mechanism of Salmonella resistance. [ABSTRACT FROM AUTHOR]

[Display omitted] • The exploration criteria were constructed through text big data mining. • Text-mining-based exploration criteria are more comprehensive and scientific. • Quantitative analyses of ore-controlling factors were completed. • Completed the deep 3D prospecting prediction by random forests model. The advent of the big data era has gradually brought about a new demand for the integration of geological text big data in three-dimensional (3D) mineral prospectivity mapping (3DMPM). Here, we report a novel workflow suitable for the application of natural language processing in 3DMPM. Our study area is the Xiyu kimberlite-type diamond deposit in Shandong, China, which has significant prospecting potential in its deep portion. In this study, exploration criteria served as a bridge connecting geological text big data and 3DMPM, and exploration criteria for the Xiyu diamond deposit were constructed employing text mining technologies. A comparative evaluation of conventional and text-mining-based exploration criteria was also conducted. Considering the text-mining-based deposit exploration criteria, we reconstructed 3D spatial anomalies and quantitatively analyzed ore-controlling factors. A random forests classification model, created using the deposit exploration criteria obtained through text mining, was applied to prospecting predictions in the Xiyu deposit, with superior results being obtained. The prediction results confirmed the applicability of the workflow and demonstrated its capacity for effective dual coupling of geological data and knowledge. [ABSTRACT FROM AUTHOR]