Effects of nutrient loading on sediment bacterial and pathogen communities within seagrass meadows

Eutrophication can play a significant role in seagrass decline and habitat loss. Microorganisms in seagrass sediments are essential to many important ecosystem processes, including nutrient cycling and seagrass ecosystem health. However, current knowledge of the bacterial communities, both beneficial and detrimental, within seagrass meadows in response to nutrient loading is limited. We studied the response of sediment bacterial and pathogen communities to nutrient enrichment on a tropical seagrass meadow in Xincun Bay, South China Sea. The bacterial taxonomic groups across all sites were dominated by the Gammaproteobacteria and Firmicutes. Sites nearest to the nutrient source and with the highest NH4+ and PO43− content had approximately double the relative abundance of putative denitrifiers Vibrionales, Alteromonadales, and Pseudomonadales. Additionally, the relative abundance of potential pathogen groups, especially Vibrio spp. and Pseudoalteromonas spp., was approximately 2‐fold greater at the sites with the highest nutrient loads compared to sites further from the source. These results suggest that proximity to sources of nutrient pollution increases the occurrence of potential bacterial pathogens that could affect fishes, invertebrates and humans. This study shows that nutrient enrichment does elicit shifts in bacterial community diversity and likely their function in local biogeochemical cycling and as a potential source of infectious diseases within seagrass meadows

Experimental investigation of mechanically laminated straight or curved-and-tapered bamboo-concrete T-beams

This study echoes the rising demand for bio-based material in concrete composite structures in the race to accelerate carbon neutrality in construction. Noticing that most previous studies are focused on straight timber or engineered bamboo-to-concrete composite beams, this study developed straight or curved-and-tapered mechanically laminated bamboo-concrete (LBC) T-beams. Six layers of 26mm thick laminated bamboo panels were glue laminated together to form the bamboo beams. The curved bamboo beams have three different rises of arch: 50mm, 100mm and 150mm. All specimen beams were tested by four-point bending tests to evaluate their structural performances of the curved and straight LBC T-beams. To monitor the flange-to-web interface shear transfer, a novel interface shear slip calibration method that captures the longitudinal after-slip strain redistribution was developed and validated by strain gauge measurements. This study also highlights the interlayer shear bonding strength of laminated bamboo as the thresholding parameter that determines the composite beams' overall flexural strength, evidenced by detailed failure mode analysis. The proposed interface shear slip calibration method can be extended to the other types of shear connectors such as screws, nails, shear plates and notched connections

Experimental exploration of five-qubit quantum error correcting code with superconducting qubits

Quantum error correction is an essential ingredient for universal quantum computing. Despite tremendous experimental efforts in the study of quantum error correction, to date, there has been no demonstration in the realisation of universal quantum error correcting code, with the subsequent verification of all key features including the identification of an arbitrary physical error, the capability for transversal manipulation of the logical state, and state decoding. To address this challenge, we experimentally realise the $[\![5,1,3]\!]$ code, the so-called smallest perfect code that permits corrections of generic single-qubit errors. In the experiment, having optimised the encoding circuit, we employ an array of superconducting qubits to realise the $[\![5,1,3]\!]$ code for several typical logical states including the magic state, an indispensable resource for realising non-Clifford gates. The encoded states are prepared with an average fidelity of $57.1(3)\%$ while with a high fidelity of $98.6(1)\%$ in the code space. Then, the arbitrary single-qubit errors introduced manually are identified by measuring the stabilizers. We further implement logical Pauli operations with a fidelity of $97.2(2)\%$ within the code space. Finally, we realise the decoding circuit and recover the input state with an overall fidelity of $74.5(6)\%$, in total with $92$ gates. Our work demonstrates each key aspect of the $[\![5,1,3]\!]$ code and verifies the viability of experimental realization of quantum error correcting codes with superconducting qubits.Comment: 6 pages, 4 figures + Supplementary Material

Influence of Intelligent Technology Applications on the Learning Effect: Virtual Reality as an Example

With the continuous application of intelligent technology, Virtual Reality (VR) technology has become a hot topic of development. VR has unique advantages in the field of the teaching due to its characteristics of immersion, interaction and sociality. Therefore, it is necessary to pay attention to the influencing factors of VR teaching on learning effect, to improve the quality of courses through improvement of relevant influencing factors and to help to reverse further development of VR technology. A questionnaire survey on influencing factors of teaching application of VR technology on the learning effect of college students was conducted. Through data collection of students of various majors and grades in multiple comprehensive universities, statistical analysis and regression empirical analysis were carried out. Results show that different majors, grades, and students’ autonomous learning ability have different influences on the teaching application of VR technology. In addition, there are many influencing factors such as network fluency, situational sense of interaction, teachers’ teaching ability and the difficulty of course design, etc. According to the conclusions, some suggestions on improving VR teaching and learning effect are put forward. It is expected to improve the learning effect and efficiency of college students under the application of VR teaching, and promote the further development of VR teaching

What drives putative bacterial pathogens removal within seagrass meadows?

To analyze the mechanism of bacterial pathogen removal in seagrass meadows, we compared bacterial pathogens abundance in trapped particles in different seagrass meadows under different intensities of human activities. We compared the particle deposition rates and abundances of bacterial pathogen in Thalassia hemprichii, Enhalus acoroides stands and adjacent unvegetated patches. The bacterial pathogens abundance was much higher in E. acoroides than in adjacent unvegetated patches, however, the trapped particles under T. hemprichii were lower than in nearby unvegetated areas with the exception of the pristine seagrass meadow. These results indicate that seagrass, at least E. acoroides, can remove bacterial pathogens by trapping particles. What is unknown, nevertheless, is how the trapped bacterial pathogens are removed by T. hemprichii. We put forward that antibacterial chemical compounds release from seagrass was stimulated by stress from human activities for inhibition of bacterial pathogen. This putative mechanism needs to be explored in future studies

Weakly supervised segmentation with maximum bipartite graph matching

In the weakly supervised segmentation task with only image-level labels, a common step in many existing algorithms is first to locate the image regions corresponding to each existing class with the Class Activation Maps (CAMs), and then generate the pseudo ground truth masks based on the CAMs to train a segmentation network in the fully supervised manner. The quality of the CAMs has a crucial impact on the performance of the segmentation model. We propose to improve the CAMs from a novel graph perspective. We model paired images containing common classes with a bipartite graph and use the maximum matching algorithm to locate corresponding areas in two images. The matching areas are then used to refine the predicted object regions in the CAMs. The experiments on Pascal VOC 2012 dataset show that our network can effectively boost the performance of the baseline model and achieves new state-of-the-art performance.AI SingaporeMinistry of Education (MOE)National Research Foundation (NRF)This work is supported by the Delta-NTU Corporate Lab with funding support from Delta Electronics Inc. and the National Research Foundation (NRF) Singapore (SMA-RP10). This work is also partly supported by the National Research Foundation Singapore under its AI Singapore Programme (Award Number: AISG-RP-2018-003) and the MOE Tier-1 research grants: RG126/17 (S), RG28/18 (S) and RG22/19 (S)