45 research outputs found
Extraction of power lines and pylons from LiDAR point clouds using a GCN-based method
The routine power line inspection is critical to maintain the reliability, availability, and sustainability of electricity supply. As a key part of inspection, power lines and pylons extraction is essential for resource management and power corridor safety, especially in the mountain regions. In this paper, we proposed a deep learning based method to extract power lines and pylons using ALS point clouds. First, a structure information preserved module is designed to mine the relationship of local neighborhood points. Then, a graph convolutional network (GCN) is used as basic module to extract point features. Finally, three categories, power lines, pylons and other objects are segmented from input point clouds. In addition, we provide an effective data enhancement strategy to generate enough samples to train the proposed model. We evaluated our method using a dataset acquired by our ALS scanning system. Experimental results demonstrate that our method is superior to the state-of-the-art methods on descriptiveness and efficiency. The overall accuracy and mean time are 99.1% and 9.3 seconds, respectively
NVIDIA FLARE: Federated Learning from Simulation to Real-World
Federated learning (FL) enables building robust and generalizable AI models
by leveraging diverse datasets from multiple collaborators without centralizing
the data. We created NVIDIA FLARE as an open-source software development kit
(SDK) to make it easier for data scientists to use FL in their research and
real-world applications. The SDK includes solutions for state-of-the-art FL
algorithms and federated machine learning approaches, which facilitate building
workflows for distributed learning across enterprises and enable platform
developers to create a secure, privacy-preserving offering for multiparty
collaboration utilizing homomorphic encryption or differential privacy. The SDK
is a lightweight, flexible, and scalable Python package. It allows researchers
to apply their data science workflows in any training libraries (PyTorch,
TensorFlow, XGBoost, or even NumPy) in real-world FL settings. This paper
introduces the key design principles of NVFlare and illustrates some use cases
(e.g., COVID analysis) with customizable FL workflows that implement different
privacy-preserving algorithms.
Code is available at https://github.com/NVIDIA/NVFlare.Comment: Accepted at the International Workshop on Federated Learning, NeurIPS
2022, New Orleans, USA (https://federated-learning.org/fl-neurips-2022);
Revised version v2: added Key Components list, system metrics for homomorphic
encryption experiment; Extended v3 for journal submissio
Self-assembled albumin nanoparticles induce pyroptosis for photodynamic/photothermal/immuno synergistic therapies in triple-negative breast cancer
Triple-negative breast cancer (TNBC) is characterized by a high degree of malignancy, early metastasis, limited treatment, and poor prognosis. Immunotherapy, as a new and most promising treatment for cancer, has limited efficacy in TNBC because of the immunosuppressive tumor microenvironment (TME). Inducing pyroptosis and activating the cyclic guanosine monophosphate-adenosine monophosphate synthase/interferon gene stimulator (cGAS/STING) signaling pathway to upregulate innate immunity have become an emerging strategy for enhancing tumor immunotherapy. In this study, albumin nanospheres were constructed with photosensitizer-IR780 encapsulated in the core and cGAS–STING agonists/H2S producer-ZnS loaded on the shell (named IR780-ZnS@HSA). In vitro, IR780-ZnS@HSA produced photothermal therapy (PTT) and photodynamic therapy (PDT) effects. In addition, it stimulated immunogenic cell death (ICD) and activated pyroptosis in tumor cells via the caspase-3–GSDME signaling pathway. IR780-ZnS@HSA also activated the cGAS–STING signaling pathway. The two pathways synergistically boost immune response. In vivo, IR780-ZnS@HSA + laser significantly inhibited tumor growth in 4T1 tumor-bearing mice and triggered an immune response, improving the efficacy of the anti-APD-L1 antibody (aPD-L1). In conclusion, IR780-ZnS@HSA, as a novel inducer of pyroptosis, can significantly inhibit tumor growth and improve the efficacy of aPD-L1
Abundance and ecological footprint of Pseudoalteromonas phage vB_PhoS_XC in the Ulva prolifera green tide
Pseudoalteromonas is a ubiquitous and abundant genus of marine bacteria commonly associated with algae. In this study, a novel siphoviral-morphological bacteriophage, vB_PhoS_XC, was isolated from the coastal seawaters of Qingdao (China) during a bloom of the Ulva prolifera (U. prolifera) green tide. The morphology of this phage (icosahedron head 51 ± 1 nm in diameter; a tail length of 86 ± 1 nm) was characterized through transmission electron microscope. The biological properties of this virus showed a short latent period (45 minutes), a large burst size (241 virions per cell) and a relatively wide range of temperatures/pH level tolerance (-20°C to 45°C and pH 4 to pH 10, respectively). The vB_PhoS_XC has a 46,490-bp double-stranded DNA genome with a G+C content of 40.0%, and encodes 72 open reading frames (ORFs). Thirty-five of these ORFs were assigned into known functions based on BLAST-based algorithm against NR database of GenBank. In addition, eco-genomic analysis provides the evidence of vB_PhoS_XC accompanied by bloom of U. prolifera, and confirmed the high expression of two phosphatase-metabolism-related auxiliary metabolic genes (AMGs). This study provides new insights into the functional and ecological roles of the Pseudoalteromonas phage vB_PhoS_XC, shedding light on the virological study approach combined with traditional isolation and meta-omics data
Large expert-curated database for benchmarking document similarity detection in biomedical literature search
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe
Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021
This online publication has been
corrected. The corrected version
first appeared at thelancet.com
on September 28, 2023BACKGROUND : Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. METHODS : Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. FINDINGS : In 2021, there were 529 million (95% uncertainty interval [UI] 500–564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8–6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7–9·9]) and, at the regional level, in Oceania (12·3% [11·5–13·0]). Nationally, Qatar had the world’s highest age-specific prevalence of diabetes, at 76·1% (73·1–79·5) in individuals aged 75–79 years. Total diabetes prevalence—especially among older adults—primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1–96·8) of diabetes cases and 95·4% (94·9–95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5–71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5–30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22–1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1–17·6) in north Africa and the Middle East and 11·3% (10·8–11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. INTERPRETATION : Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers.Bill & Melinda Gates Foundation.http://www.thelancet.comam2024School of Health Systems and Public Health (SHSPH)SDG-03:Good heatlh and well-bein
Aligning territorial spatial planning with sustainable development goals: A comprehensive analysis of production, living, and ecological spaces in China
Territorial spatial planning serves a primary instrument for optimizing the utilization of production, living, and ecological spaces (PLEs), significantly influencing on social, economic, and ecological sustainability. The correlation aligns with the objectives outlined in the Sustainable Development Goals (SDGs) within the 2030 Agenda for Sustainable Development. To comprehensively grasp the impact of territorial spatial planning on SDGs, particularly how changes in land use types and functions shape sustainable development, our study employed data encompassing land use and SDG scores across 31 provinces in China from 2000 to 2015. Utilizing Spearman coefficients, we meticulously examined the relationship between PLEs and SDGs. Our findings illuminated that PLEs based on land function (PLEs-LF) wield a more substantial influence on SDGs compared to those grounded in the proportion of land use types (PLEs-LUT). The former exhibited synergistic or trade-off relationships with 16 SDGs, whereas the latter demonstrated associations with 14 SDGs, underscoring the importance of concurrently considering both land use space and function in land sustainability research. Moreover, the positive impact of living space and function on social SDGs, particularly in poverty reduction, health, and gender equality, contrasts with ecological space and function, which exhibits trade-offs. The economic realm manifests no significant synergy or trade-off between production space and economic SDGs. Additionally, it is imperative not to overlook the preservation of fostering sustainable development in the oceans while simultaneously terrestrial ecological diversity. Our research imparts valuable insights, particularly for developing countries like China, facilitating a more effective implementation of SDG practices in land and providing guidance for territorial spatial governance and planning
Engineering of lithium-metal anodes towards a safe and stable battery
Currently, the state-of-the-art lithium-ion batteries (LIBs) are the most widely used energy storage devices and have brought a great impact on our daily life. However, even many strategies have been reported to improve the energy density, these LIBs still can not meet the rapidly growing demand from the many lately emerged devices. During the pursue of higher energy densities, lithium-metal batteries (LMBs) have been the most promising candidates of the next-generation energy storage devices. Unfortunately, the Li-metal anode usually induces severe safety concerns and inferior cycle performance, because of the dendrite growth, high reactivity, and infinite volume changes of Li metal. As a result, these problems limit the commercial application of LMBs and must be resolved prior to the practical deployment of LMBs. In this review, we will firstly discuss the failure mechanisms of Li-metal anodes and introduce latest characterization technologies to study dendritic Li formation. The advances to improve the safety and performance of Li metal anode through electrolyte modification, interfacial engineering, solid-state electrolyte incorporation, and host materials design will then be comprehensively summarized and discussed. Lastly, we will conclude by summarizing the challenges in the current research on LMBs and highlight the future perspectives as well. Through this review, we hope to present the latest developments of the Li metal anode materials for the readers, and also shed light on the possible solutions for the current issues in order to accelerate both fundamental research and practical deployment of the various LMBs
Oridonin Inhibits <i>Mycobacterium marinum</i> Infection-Induced Oxidative Stress In Vitro and In Vivo
Prior to the COVID-19 pandemic, tuberculosis (TB) was the leading cause of death globally attributable to a single infectious agent, ranking higher than HIV/AIDS. Consequently, TB remains an urgent public health crisis worldwide. Oridonin (7a,20-Epoxy-1a,6b,7,14-tetrahydroxy-Kaur-16-en-15-one Isodonol, C20H28O6, Ori), derived from the Rabdosia Rrubescens plant, is a natural compound that exhibits antioxidant, anti-inflammatory, and antibacterial properties. Our objective was to investigate whether Ori’s antioxidant and antibacterial effects could be effective against the infection Mycobacterium marinum (Mm)-infected cells and zebrafish. We observed that Ori treatment significantly impeded Mm infection in lung epithelial cells, while also suppressing inflammatory response and oxidative stress in Mm-infected macrophages. Further investigation revealed that Ori supplementation inhibited the proliferation of Mm in zebrafish, as well as reducing oxidative stress levels in infected zebrafish. Additionally, Ori promoted the expression of NRF2/HO-1/NQO-1 and activated the AKT/AMPK-α1/GSK-3β signaling pathway, which are both associated with anti-inflammatory and antioxidant effects. In summary, our results demonstrate that Ori exerts inhibitory effects on Mm infection and proliferation in cells and zebrafish, respectively. Additionally, Ori regulates oxidative stress by modulating the NRF2/HO-1/NQO-1 and AKT/AMPK-α1/GSK-3β signaling pathways