Research Progress on Nano-Delivered Plant Polyphenols in the Prevention and Treatment of Alzheimer’s Disease

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases with a complex pathological mechanism, and as the incidence of AD has increased recently, there is an urgent need to develop more effective prevention and treatment methods. Many studies have shown that plant polyphenols have great potential in the prevention and treatment of neurodegenerative diseases, but their bioavailability is poor, limiting their practical applications. The application of nanotechnology can be helpful for the delivery of plant polyphenols. This article aims to elaborate recent progress and challenges in the development of nano-delivery systems for plant polyphenols, and review the common plant polyphenols used for AD treatment and their action mechanisms

Seizing the window of opportunity to mitigate the impact of climate change on the health of Chinese residents

The health threats posed by climate change in China are increasing rapidly. Each province faces different health risks. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate and even prevent the achievement of the Healthy and Beautiful China initiatives. The 2021 China Report of the Lancet Countdown on Health and Climate Change is the first annual update of China’s Report of the Lancet Countdown. It comprehensively assesses the impact of climate change on the health of Chinese households and the measures China has taken. Invited by the Lancet committee, Tsinghua University led the writing of the report and cooperated with 25 relevant institutions in and outside of China. The report includes 25 indicators within five major areas (climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement) and a policy brief. This 2021 China policy brief contains the most urgent and relevant indicators focusing on provincial data: The increasing health risks of climate change in China; mixed progress in responding to climate change. In 2020, the heatwave exposures per person in China increased by 4.51 d compared with the 1986–2005 average, resulting in an estimated 92% increase in heatwave-related deaths. The resulting economic cost of the estimated 14500 heatwave-related deaths in 2020 is US$176 million. Increased temperatures also caused a potential 31.5 billion h in lost work time in 2020, which is equivalent to 1.3% of the work hours of the total national workforce, with resulting economic losses estimated at 1.4% of China’s annual gross domestic product. For adaptation efforts, there has been steady progress in local adaptation planning and assessment in 2020, urban green space growth in 2020, and health emergency management in 2019. 12 of 30 provinces reported that they have completed, or were developing, provincial health adaptation plans. Urban green space, which is an important heat adaptation measure, has increased in 18 of 31 provinces in the past decade, and the capacity of China’s health emergency management increased in almost all provinces from 2018 to 2019. As a result of China’s persistent efforts to clean its energy structure and control air pollution, the premature deaths due to exposure to ambient particulate matter of 2.5 μm or less (PM2.5) and the resulting costs continue to decline. However, 98% of China’s cities still have annual average PM2.5 concentrations that are more than the WHO guideline standard of 10 μg/m3. It provides policymakers and the public with up-to-date information on China’s response to climate change and improvements in health outcomes and makes the following policy recommendations. (1) Promote systematic thinking in the related departments and strengthen multi-departmental cooperation. Sectors related to climate and development in China should incorporate health perspectives into their policymaking and actions, demonstrating WHO’s and President Xi Jinping’s so-called health-in-all-policies principle. (2) Include clear goals and timelines for climate-related health impact assessments and health adaptation plans at both the national and the regional levels in the National Climate Change Adaptation Strategy for 2035. (3) Strengthen China’s climate mitigation actions and ensure that health is included in China’s pathway to carbon neutrality. By promoting investments in zero-carbon technologies and reducing fossil fuel subsidies, the current rebounding trend in carbon emissions will be reversed and lead to a healthy, low-carbon future. (4) Increase awareness of the linkages between climate change and health at all levels. Health professionals, the academic community, and traditional and new media should raise the awareness of the public and policymakers on the important linkages between climate change and health.</p

Genetic diversity fuels gene discovery for tobacco and alcohol use

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury(1-4). These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries(5). Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.Peer reviewe

Lpnet: Reconstructing phylogenetic networks from distances using integer linear programming

Abstract Neighbor‐net is a widely used network reconstructing method that approximates pairwise distances between taxa by a circular phylogenetic network. We present Lpnet, a variant of Neighbor‐net. We first apply standard methods to construct a binary phylogenetic tree and then use integer linear programming to compute an optimal circular ordering that agrees with all tree splits. This approach achieves an improved approximation of the input distance for the clear majority of experiments that we have run for simulated and real data. We release an implementation in R that can handle up to 94 taxa and usually needs about 1 min on a standard computer for 80 taxa. For larger taxa sets, we include a top‐down heuristic which also tends to perform better than Neighbor‐net. Our Lpnet provides an alternative to Neighbor‐net and performs better in most cases. We anticipate Lpent will be useful to generate phylogenetic hypotheses

Degradation Mechanism of Concrete Subjected to External Sulfate Attack: Comparison of Different Curing Conditions

Sulfate induced degradation of concrete brings great damage to concrete structures in saline or offshore areas. The degradation mechanism of cast-in-situ concrete still remains unclear. This paper investigates the degradation process and corresponding mechanism of cast-in-situ concrete when immersed in sulfate-rich corrosive environments. Concrete samples with different curing conditions were prepared and immersed in sulfate solutions for 12 months to simulate the corrosion of precast and cast-in-situ concrete structures, respectively. Tests regarding the changes of physical, chemical, and mechanical properties of concrete samples were conducted and recorded continuously during the immersion. Micro-structural and mineral methods were performed to analyze the changes of concrete samples after immersion. Results indicate that the corrosion process of cast-in-situ concrete is much faster than the degradation of precast concrete. Chemical attack is the main cause of degradation for both precast and cast-in-situ concrete. Concrete in the environment with higher sulfate concentration suffers more severe degradation. The water/cement ratio has a significant influence on the durability of concrete. A lower water/cement ratio results in obviously better resistance against sulfate attack for both precast and cast-in-situ concrete

Editorial of the Special Issue of the 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA’12)

International audienc

Electrical field distribution of 35 KV Igla under polluted and ice-covered situation at power frequency

Lightning striking on power system may lead to severe accident and huge losses, which is the reason why line arresters are needed. During operation, the electrical field concentration in line arrester may lead to ablation and accelerated aging of insulation materials, and furthermore, cause mal-operation. A type of line arresters with internal series air gap (IGLA) is chosen by this study, which is distinguished from the previously used metallic oxide arrester, with better characteristic on anti-flashover when icing, but with worse characteristic on anti-flashover when polluted. This paper aims at studying the probable variation of power-frequency electric field of this new kind of IGLA under polluted and icing situation, and making further analysis on the reason why the electric field strength change like this

Recent Advances in Organic Photovoltaic Materials Based on Thiazole-Containing Heterocycles

Organic solar cells (OSCs) have achieved great progress, driven by the rapid development of wide bandgap electron donors and narrow bandgap non-fullerene acceptors (NFAs). Among a large number of electron-accepting (A) building blocks, thiazole (Tz) and its derived fused heterocycles have been widely used to construct photovoltaic materials, especially conjugated polymers. Benefiting from the electron deficiency, rigidity, high planarity, and enhanced intra/intermolecular interactions of Tz-containing heterocycles, some related photovoltaic materials exhibit proper energy levels, optimized molecular aggregation, and active layer morphology, leading to excellent photovoltaic performance. This review focuses on the progress of Tz-based photovoltaic materials in the field of OSCs. First, the Tz-based donor and acceptor photovoltaic materials are reviewed. Then, the materials based on promising Tz-containing heterocycles, mainly including thiazolo[5,4-d]thiazole (TzTz), benzo[1,2-d:4,5-d’]bis(thiazole) (BBTz), and benzo[d]thiazole (BTz) are summarized and discussed. In addition, the new emerging Tz-fused structures and their application in OSCs are introduced. Finally, perspectives and outlooks for the further development of Tz-containing heterocycle-based photovoltaic materials are proposed

A Recurrent Adaptive Network: Balanced Learning for Road Crack Segmentation with High-Resolution Images

Road crack segmentation based on high-resolution images is an important task in road service maintenance. The undamaged road surface area is much larger than the damaged area on a highway. This imbalanced situation yields poor road crack segmentation performance for convolutional neural networks. In this paper, we first evaluate the mainstream convolutional neural network structure in the road crack segmentation task. Second, inspired by the second law of thermodynamics, an improved method called a recurrent adaptive network for a pixelwise road crack segmentation task is proposed to solve the extreme imbalance between positive and negative samples. We achieved a flow between precision and recall, similar to the conduction of temperature repetition. During the training process, the recurrent adaptive network (1) dynamically evaluates the degree of imbalance, (2) determines the positive and negative sampling rates, and (3) adjusts the loss weights of positive and negative features. By following these steps, we established a channel between precision and recall and kept them balanced as they flow to each other. A dataset of high-resolution road crack images with annotations (named HRRC) was built from a real road inspection scene. The images in HRRC were collected on a mobile vehicle measurement platform by high-resolution industrial cameras and were carefully labeled at the pixel level. Therefore, this dataset has sufficient data complexity to objectively evaluate the real performance of convolutional neural networks in highway patrol scenes. Our main contribution is a new method of solving the data imbalance problem, and the method of guiding model training by analyzing precision and recall is experimentally demonstrated to be effective. The recurrent adaptive network achieves state-of-the-art performance on this dataset

A new multi-source remote sensing image sample dataset with high resolution for flood area extraction: GF-FloodNet

Deep learning algorithms show good prospects for remote sensing flood monitoring. They mostly rely on huge amounts of labeled data. However, there is a lack of available labeled data in actual needs. In this paper, we propose a high-resolution multi-source remote sensing dataset for flood area extraction: GF-FloodNet. GF-FloodNet contains 13388 samples from Gaofen-3 (GF-3) and Gaofen-2 (GF-2) images. We use a multi-level sample selection and interactive annotation strategy based on active learning to construct it. Compare with other flood-related datasets, GF-FloodNet not only has a spatial resolution of up to 1.5 m and provides pixel-level labels, but also consists of multi-source remote sensing data. We thoroughly validate and evaluate the dataset using several deep learning models, including quantitative analysis, qualitative analysis, and validation on large-scale remote sensing data in real scenes. Experimental results reveal that GF-FloodNet has significant advantages by multi-source data. It can support different deep learning models for training to extract flood areas. There should be a potential optimal boundary for model training in any deep learning dataset. The boundary seems close to 4824 samples in GF-FloodNet. We provide GF-FloodNet at https://www.kaggle.com/datasets/pengliuair/gf-floodnet and https://pan.baidu.com/s/1vdUCGNAfFwG5UjZ9RLLFMQ?pwd=8v6o