Insights into the roles of bacterial infection and antibiotics in Parkinson’s disease

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which is accompanied with the classical motor symptoms and a range of non-motor symptoms. Bacterial infection affects the neuroinflammation associated with the pathology of PD and various antibiotics have also been confirmed to play an important role not only in bacterial infection, but also in the PD progression. This mini-review summarized the role of common bacterial infection in PD and introduced several antibiotics that had anti-PD effects

The cooling intensity dependent on landscape complexity of green infrastructure in the metropolitan area

The cooling effect of green infrastructure (GI) is becoming a hot topic on mitigating the urban heat island (UHI) effect. Alterations to the green space are a viable solution for reducing land surface temperature (LST), yet few studies provide specific guidance for landscape planning adapted to the different regions. This paper proposed and defined the landscape complexity and the threshold value of cooling effect (TVoE). Results find that: (1) GI provides a better cooling effect in the densely built-up area than the green belt; (2) GI with a simple form, aggregated configuration, and low patch density had a better cooling intensity; (3) In the densely built-up area, TVoE of the forest area is 4.5 ha, while in the green belt, TVoE of the forest and grassland area is 9 ha and 2.25 ha. These conclusions will help the planners to reduce LST effectively, and employ environmentally sustainable planning

Multimodal machine learning for materials science: composition-structure bimodal learning for experimentally measured properties

The widespread application of multimodal machine learning models like GPT-4 has revolutionized various research fields including computer vision and natural language processing. However, its implementation in materials informatics remains underexplored, despite the presence of materials data across diverse modalities, such as composition and structure. The effectiveness of machine learning models trained on large calculated datasets depends on the accuracy of calculations, while experimental datasets often have limited data availability and incomplete information. This paper introduces a novel approach to multimodal machine learning in materials science via composition-structure bimodal learning. The proposed COmposition-Structure Bimodal Network (COSNet) is designed to enhance learning and predictions of experimentally measured materials properties that have incomplete structure information. Bimodal learning significantly reduces prediction errors across distinct materials properties including Li conductivity in solid electrolyte, band gap, refractive index, dielectric constant, energy, and magnetic moment, surpassing composition-only learning methods. Furthermore, we identified that data augmentation based on modal availability plays a pivotal role in the success of bimodal learning

Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery

When taking images against strong light sources, the resulting images often contain heterogeneous flare artifacts. These artifacts can importantly affect image visual quality and downstream computer vision tasks. While collecting real data pairs of flare-corrupted/flare-free images for training flare removal models is challenging, current methods utilize the direct-add approach to synthesize data. However, these methods do not consider automatic exposure and tone mapping in image signal processing pipeline (ISP), leading to the limited generalization capability of deep models training using such data. Besides, existing methods struggle to handle multiple light sources due to the different sizes, shapes and illuminance of various light sources. In this paper, we propose a solution to improve the performance of lens flare removal by revisiting the ISP and remodeling the principle of automatic exposure in the synthesis pipeline and design a more reliable light sources recovery strategy. The new pipeline approaches realistic imaging by discriminating the local and global illumination through convex combination, avoiding global illumination shifting and local over-saturation. Our strategy for recovering multiple light sources convexly averages the input and output of the neural network based on illuminance levels, thereby avoiding the need for a hard threshold in identifying light sources. We also contribute a new flare removal testing dataset containing the flare-corrupted images captured by ten types of consumer electronics. The dataset facilitates the verification of the generalization capability of flare removal methods. Extensive experiments show that our solution can effectively improve the performance of lens flare removal and push the frontier toward more general situations.Comment: ICCV 202