A Bayesian benchmark concentration analysis for urinary fluoride and intelligence in adults in Guizhou, China.

Environmental fluoride exposure has been linked to numerous cases of fluorosis worldwide. Previous studies have indicated that long-term exposure to fluoride can result in intellectual damage among children. However, a comprehensive health risk assessment of fluorosis-induced intellectual damage is still pending. In this research, we utilized the Bayesian Benchmark Dose Analysis System (BBMD) to investigate the dose-response relationship between urinary fluoride (U-F) concentration and Raven scores in adults from Nayong, Guizhou, China. Our research findings indecate a dose-response relationship between the concentration of U-F and intelligence scores in adults. As the benchmark response (BMR) increased, both the benchmark concentration (BMCs) and the lower bound of the credible interval (BMCLs) increased. Specifically, BMCs for the association between U-F and IQ score were determined to be 0.18 mg/L (BMCL1 = 0.08 mg/L), 0.91 mg/L (BMCL5 = 0.40 mg/L), 1.83 mg/L (BMCL10 = 0.83 mg/L) when using BMRs of 1 %, 5 %, and 10 %. These results indicate that U-F can serve as an effective biomarker for monitoring the loss of IQ in population. We propose three interim targets for public policy in preventing interllectual harm from fluoride exposure.

DMSOP-cleaving enzymes are diverse and widely distributed in marine microorganisms.

Dimethylsulfoxonium propionate (DMSOP) is a recently identified and abundant marine organosulfur compound with roles in oxidative stress protection, global carbon and sulfur cycling and, as shown here, potentially in osmotolerance. Microbial DMSOP cleavage yields dimethyl sulfoxide, a ubiquitous marine metabolite, and acrylate, but the enzymes responsible, and their environmental importance, were unknown. Here we report DMSOP cleavage mechanisms in diverse heterotrophic bacteria, fungi and phototrophic algae not previously known to have this activity, and highlight the unappreciated importance of this process in marine sediment environments. These diverse organisms, including Roseobacter, SAR11 bacteria and Emiliania huxleyi, utilized their dimethylsulfoniopropionate lyase 'Ddd' or 'Alma' enzymes to cleave DMSOP via similar catalytic mechanisms to those for dimethylsulfoniopropionate. Given the annual teragram predictions for DMSOP production and its prevalence in marine sediments, our results highlight that DMSOP cleavage is likely a globally significant process influencing carbon and sulfur fluxes and ecological interactions.

Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators.

By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.