Fate of glyphosate in lakes with varying trophic levels and its modification by root exudates of submerged macrophytes.

Accelerated eutrophication in lakes reduces the number of submerged macrophytes and alters the residues of glyphosate and its degradation products. However, the effects of submerged macrophytes on the fate of glyphosate remain unclear. We investigated eight lakes with varying trophic levels along the middle and lower reaches of the Yangtze River in China, of which five lakes contained either glyphosate or aminomethylphosphate (AMPA). Glyphosate and AMPA residues were significantly positively correlated with the trophic levels of lakes (P < 0.01). In lakes, glyphosate is degraded through the AMPA and sarcosine pathways. Eight shared glyphosate-degrading enzymes and genes were observed in different lake sediments, corresponding to 44 degrading microorganisms. Glyphosate concentrations in sediments were significantly higher in lakes with lower abundances of soxA (sarcosine oxidase) and soxB (sarcosine oxidase) (P < 0.05). In the presence of submerged macrophytes, oxalic and malonic acids secreted by the roots of submerged macrophytes increased the abundance of glyphosate-degrading microorganisms containing soxA or soxB (P < 0.05). These results revealed that a decrease in the number of submerged macrophytes in eutrophic lakes may inhibit glyphosate degradation via the sarcosine pathway, leading to a decrease in glyphosate degradation and an increase in glyphosate residues.

Influence of Preheating Temperature on the Microstructure and Mechanical Properties of 6061/TA1 Composite Plates Fabricated by AFSD.

In this study, composite plates of 6061/TA1 were successfully manufactured using additive friction stir deposition (AFSD). The impact of preheating temperatures (room temperature, 100 °C, 200 °C) on the interfacial microstructure and interface mechanical properties at various deposition zones was studied. The results showed that as the preheating temperature increased or when the deposit zone shifted from the boundary to the center, the diffusion width of Al and Ti increased, accompanied by an increase in bonding shear strength. Moreover, in the boundary zone of the sample preheated at room temperature (P-RT), only mechanical bonding was observed, resulting in the lowest bonding shear strength. Conversely, the other samples exhibited a combination of mechanical and metallurgical bonding. Under the preheating temperature of 200 °C, interfacial intermetallic compounds were observed near the center zone, which exhibited the highest bonding shear strength.

Monocular camera and IMU integration for indoor position estimation.

This paper presents a monocular camera (MC) and inertial measurement unit (IMU) integrated approach for indoor position estimation. Unlike the traditional estimation methods, we fix the monocular camera downward to the floor and collect successive frames where textures are orderly distributed and feature points robustly detected, rather than using forward oriented camera in sampling unknown and disordered scenes with pre-determined frame rate and auto-focus metric scale. Meanwhile, camera adopts the constant metric scale and adaptive frame rate determined by IMU data. Furthermore, the corresponding distinctive image feature point matching approaches are employed for visual localizing, i.e., optical flow for fast motion mode; Canny Edge Detector & Harris Feature Point Detector & Sift Descriptor for slow motion mode. For superfast motion and abrupt rotation where images from camera are blurred and unusable, the Extended Kalman Filter is exploited to estimate IMU outputs and to derive the corresponding trajectory. Experimental results validate that our proposed method is effective and accurate in indoor positioning. Since our system is computationally efficient and in compact size, it's well suited for visually impaired people indoor navigation and wheelchaired people indoor localization.

Unsupervised tumour segmentation in PET using local and global intensity-fitting active surface and alpha matting.

This paper proposes an unsupervised tumour segmentation approach for PET data. The method computes the volumes of interest (VOIs) with sub-voxel precision by considering the limited image resolution and partial volume effects. First, an improved anisotropic diffusion filter is used to remove image noise. A hierarchical local and global intensity active surface modelling scheme is then applied to segment VOIs, followed by an alpha matting step to further refine the segmentation boundary. The proposed method is validated on real PET images of head-and-neck cancer patients with ground truth provided by human experts, as well as custom-designed phantom PET images with objective ground truth. Experimental results show that our method outperforms previous automatic approaches in terms of segmentation accuracy.

Comparative study with new accuracy metrics for target volume contouring in PET image guided radiation therapy.

The impact of PET on radiation therapy is held back by poor methods of defining functional volumes of interest. Many new software tools are being proposed for contouring target volumes but the different approaches are not adequately compared and their accuracy is poorly evaluated due to the illdefinition of ground truth. This paper compares the largest cohort to date of established, emerging and proposed PET contouring methods, in terms of accuracy and variability. We emphasise spatial accuracy and present a new metric that addresses the lack of unique ground truth. 30 methods are used at 13 different institutions to contour functional VOIs in clinical PET/CT and a custom-built PET phantom representing typical problems in image guided radiotherapy. Contouring methods are grouped according to algorithmic type, level of interactivity and how they exploit structural information in hybrid images. Experiments reveal benefits of high levels of user interaction, as well as simultaneous visualisation of CT images and PET gradients to guide interactive procedures. Method-wise evaluation identifies the danger of over-automation and the value of prior knowledge built into an algorithm.

Unsupervised tumour segmentation in PET based on local and global intensity fitting active surface and alpha matting.

This paper proposes an unsupervised tumour segmentation scheme for PET data. The method computes the volume of interests (VOIs) with subpixel precision by considering the limited resolution and partial volume effect. Firstly, it uses local and global intensity active surface modelling to segment VOIs, then an alpha matting method is used to eliminate false negative classification and refine the segmentation results. We have validated our method on real PET images of head-and-neck cancer patients as well as images of a custom designed PET phantom. Experiments show that our method can generate more accurate segmentation results compared with alternative approaches.