Homogenisation of water and sediment bacterial communities in a shallow lake (lake Balihe, China)

Planktonic and benthic bacterial communities hold central roles in the functioning of freshwater ecosystems and mediate key ecosystem services such as primary production and nutrient remineralisation. Although it is clear that such communities vary in composition both within and between lakes, the environmental factors and processes shaping the diversity and composition of freshwater bacteria are still not fully understood. In order to assess seasonal and spatial variability in lake bacterial communities and identify environmental factors underpinning biogeographical patterns, we performed a large-scale sampling campaign with paired water and sediment sample collection at 18 locations during four seasons in Lake Balihe, a subtropical shallow fish-farming lake in mid-eastern China. Pelagic and benthic bacterial communities were distinctly different in terms of diversity, taxonomic composition and community structure, with Actinobacteria, Bacteroidetes, Cyanobacteria and Alphaproteobacteria dominating lake water, and Acidobacteria, Bacteroidetes, Chloroflexi, Gammaproteobacteria and Deltaproteobacteria dominating sediment. Nevertheless, these two communities had stronger spatial concordance and overlap in taxa during spring and autumn seasons. Together, the main drivers of both the spatial and temporal variations in Lake Balihe bacterial communities were identified as water temperature, turbidity, nitrogen and phosphorus availability, and thermal stratification controlled by wind-mixing and activity of the dense farmed fish populations. Notably, populations affiliated with Firmicutes, known to be abundant in fish gut microbiome, were especially abundant in the summer season and locations where high fish biomass was found, suggesting a potential link between fish gut microbiome and the pelagic bacterial communities. Our findings demonstrated seasonal homogenisation of pelagic and benthic bacterial communities linked to marked shifts in a set of seasonally-driven environmental variables including water temperature and nutrient availability

A new strategy for controlling invasive weeds: selecting valuable native plants to defeat them

To explore replacement control of the invasive weed Ipomoea cairica, we studied the competitive effects of two valuable natives, Pueraria lobata and Paederia scandens, on growth and photosynthetic characteristics of I. cairica, in pot and field experiments. When I. cairica was planted in pots with P. lobata or P. scandens, its total biomass decreased by 68.7% and 45.8%, and its stem length by 33.3% and 34.1%, respectively. The two natives depressed growth of the weed by their strong effects on its photosynthetic characteristics, including suppression of leaf biomass and the abundance of the CO 2 -fixing enzyme RUBISCO. The field experiment demonstrated that sowing seeds of P. lobata or P. scandens in plots where the weed had been largely cleared produced 11.8-fold or 2.5-fold as much leaf biomass of the two natives, respectively, as the weed. Replacement control by valuable native species is potentially a feasible and sustainable means of suppressing I. cairica

Multi-parameter MRI radiomic features may contribute to predict progression-free survival in patients with WHO grade II meningiomas

AimThis study aims to investigate the potential value of radiomic features from multi-parameter MRI in predicting progression-free survival (PFS) of patients with WHO grade II meningiomas.MethodsKaplan–Meier survival curves were used for survival analysis of clinical features. A total of 851 radiomic features were extracted based on tumor region segmentation from each sequence, and Max-Relevance and Min-Redundancy (mRMR) algorithm was applied to filter and select radiomic features. Bagged AdaBoost, Stochastic Gradient Boosting, Random Forest, and Neural Network models were built based on selected features. Discriminative abilities of models were evaluated using receiver operating characteristics (ROC) and area under the curve (AUC).ResultsOur study enrolled 164 patients with WHO grade II meningiomas. Female gender (p=0.023), gross total resection (GTR) (p<0.001), age <68 years old (p=0.023), and edema index <2.3 (p=0.006) are protective factors for PFS in these patients. Both the Bagged AdaBoost model and the Neural Network model achieved the best performance on test set with an AUC of 0.927 (95% CI, Bagged AdaBoost: 0.834–1.000; Neural Network: 0.836–1.000).ConclusionThe Bagged AdaBoost model and the Neural Network model based on radiomic features demonstrated decent predictive ability for PFS in patients with WHO grade II meningiomas who underwent operation using preoperative multi-parameter MR images, thus bringing benefit for patient prognosis prediction in clinical practice. Our study emphasizes the importance of utilizing advanced imaging techniques such as radiomics to improve personalized treatment strategies for meningiomas by providing more accurate prognostic information that can guide clinicians toward better decision-making processes when treating their patients’ conditions effectively while minimizing risks associated with unnecessary interventions or treatments that may not be beneficial