The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises

The Lancet Countdown is an international collaboration, established to provide an independent, global monitoring system dedicated to tracking the emerging health profile of the changing climate. The 2020 report presents 43 indicators across five sections: 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. This report represents the findings and consensus of the 35 leading academic institutions and UN agencies that make up the Lancet Countdown, and draws on the expertise of climate scientists, geographers, and engineers; of energy, food, and transport experts; and of economists, social and political scientists, data scientists, public health professionals, and doctors

A Decision-Making Method for Distributed Unmanned Aerial Vehicle Swarm considering Attack Constraints in the Cooperative Strike Phase

In view of the growing military forces of various countries, unmanned aerial vehicle (UAV) swarms, as a new type of weapon, are gradually attracting the attention of more and more countries. Decision-making, as the core link in its application, has also become the focus of research in these countries. In this work, the distributed UAV swarm cooperative strike decision-making problems are separated from the distributed UAV swarm cooperative search strike decision-making problems, and the distributed UAV swarm cooperative strike multiobjective decision-making model is established, with the relevant constraints clarified. Besides, according to the analysis of the motion characteristics of UAV and the speed requirements of the distributed UAV swarm cooperative strike decision, an arc tangent trajectory planning method, conforming to the motion characteristics of UAV, is proposed. Moreover, a distributed cooperative strike decision-making method, based on the idea of “campaign endorsement and invitation cooperation,” is put forward, with the effectiveness and superiority validated by simulation experiments

Linear building pattern recognition in topographical maps combining convex polygon decomposition

Building patterns are crucial for urban form understanding, automated map generalization, and 3 D city model visualization. The existing studies have recognized various building patterns based on visual perception rules in which buildings are considered as a whole. However, some visually aware patterns may fail to be recognized with these approaches because human vision is also proved as a part-based system. This paper first proposed an approach for linear building pattern recognition combining convex polygon decomposition. Linear building patterns including collinear patterns and curvilinear patterns are defined according to the proximity, similarity, and continuity between buildings. Linear building patterns are then recognized by combining convex polygon decomposition, in which a building can be decomposed into sub-buildings for pattern recognition. A novel node concavity is developed based on polygon skeletons which is applicable for building polygons with holes or not in the building decomposition. And building’s orthogonal features are also considered in the building decomposition. Two datasets collected from Ordnance Survey (OS) were used in the experiments to verify the effectiveness of the proposed approach. The results indicate that our approach achieves 25.57% higher precision and 32.23% higher recall in collinear pattern recognition and 15.67% higher precision and 18.52% higher recall in curvilinear pattern recognition when compared to existing approaches. Recognition of other kinds of building patterns including T-shaped and C-shaped patterns combining convex polygon decomposition are also discussed in this approach

Design and Numerical Study of Argon Gas Diversion System Using Orthogonal Experiment to Reduce Impurities in Large-Sized Casting Silicon

To reduce oxygen and carbon impurities while casting silicon, an argon gas diversion system is proposed. A series of two-dimensional global transient numerical simulations are carried out using Fluent software according to the orthogonal experimental design, including heat transfer, convection of silicon melt and argon gas, and the fully coupling transport of impurities. The numerical results show that when the distance between the outer tube outlet and the cover is 10 mm, the backflow is inhibited by lateral outflow, thus the generation of CO is suppressed and the penetration of impurities into the silicon melt is decreased. The larger the flow rate, the more obvious the effect is. When the outer tube outlet is far from the cover, the effect of removing impurities is no longer significant. In addition, too large or too small an inner tube flow rate is not conducive to impurity reduction. The optimal parameter combination of outer tube flow rate, inner tube flow rate, and the distance between outer tube outlet and the cover are determined by the orthogonal experiment. Compared with the original furnace, the average concentration of oxygen and carbon in casting silicon ingots could be decreased by 7.4% and 59.9%, respectively, by using the optimized argon gas diversion system

Preparative Purification of Linarin Extracts from Dendranthema indicum Flowers and Evaluation of Its Antihypertensive Effect

Background. Preliminary research showed that linarin (LIN) might have a relationship with the antihypertensive effect of Dendranthema indicum flowers. However, the preparative method for LIN enriched extract from Dendranthema indicum flowers was not clear and its antihypertensive effect was not confirmed. In this study, we designed a series of experiments to develop an efficient method for purification of LIN extracts and confirm the possibility of LIN extracts to be an antihypertensive drug. Materials and Methods. HPLC-VWD/DAD were used in the process of developing purification method. The antihypertensive effect of LIN extracts was tested by CODA Mouse & Rat Tail-Cuff Blood Pressure System; western blot and biochemical analysis were used to investigate mechanism and toxicity. Results. The content and recovery of LIN reached 55.68±2.08% and 66.65±1.73%, respectively, through solid-liquid extraction. The composition of product was stable through the analysis of fingerprint. Chronic administration of LIN extracts reduced blood pressure obviously which had a relationship with the inhibition of renin-angiotensin system (RAS) in kidney and the function indexes of kidney and liver had no variations. Conclusions. The preparation method was simple, low-cost, and stable, and it was fit for industrial application. The LIN prepared by this method had the potential to be an antihypertensive drug

Integrative network fusion-based multi-omics study for biomarker identification and patient classification of rheumatoid arthritis

Abstract Background Cold-dampness Syndrome (RA-Cold) and Hot-dampness Syndrome (RA-Hot) are two distinct groups of rheumatoid arthritis (RA) patients with different clinical symptoms based on traditional Chinese medicine (TCM) theories and clinical empirical knowledge. However, the biological basis of the two syndromes has not been fully elucidated, which may restrict the development of personalized medicine and drug discovery for RA diagnosis and therapy. Methods An integrative strategy combining clinical transcriptomics, phenomics, and metabolomics data based on clinical cohorts and adjuvant-induced arthritis rat models was performed to identify novel candidate biomarkers and to investigate the biological basis of RA-Cold and RA-Hot. Results The main clinical symptoms of RA-Cold patients are joint swelling, pain, and contracture, which may be associated with the dysregulation of T cell-mediated immunity, osteoblast differentiation, and subsequent disorders of steroid biosynthesis and phenylalanine metabolism. In contrast, the main clinical symptoms of RA-Hot patients are fever, irritability, and vertigo, which may be associated with various signals regulating angiogenesis, adrenocorticotropic hormone release, and NLRP3 inflammasome activation, leading to disorders of steroid biosynthesis, nicotinamide, and sphingolipid metabolism. IL17F, 5-HT, and IL4I1 were identified as candidate biomarkers of RA-Cold, while S1P and GLNS were identified as candidate biomarkers of RA-Hot. Conclusions The current study presents the most comprehensive metabonomic and transcriptomic profiling of serum, urine, synovial fluid, and synovial tissue samples obtained from RA-Cold and RA-Hot patients and experimental animal models to date. Through the integration of multi-omics data and clinical independent validation, a list of novel candidate biomarkers of RA-Cold and RA-Hot syndromes were identified, that may be useful in improving RA diagnosis and therapy

Multi-Omics Analysis of GNL3L Expression, Prognosis, and Immune Value in Pan-Cancer

Guanine nucleotide-binding protein-like 3-like protein (GNL3L) is a novel, evolutionarily conserved, GTP-binding nucleolar protein. This study aimed to investigate the expression, prognosis, and immune value of GNL3L in pan-cancer from multiple omics analyses. Firstly, the expression and prognostic value of GNL3L in pan-cancer were discussed using the TIMER2 database, the GEPIA database, the cBioportal database, COX regression analysis, and enrichment analysis. The association of GNL3L with tumor mutational burden (TMB), tumor microsatellite instability (MSI), mismatch repair (MMR) genes, and immune cells was then analyzed. Finally, an esophageal cancer (ESCA) prediction model was established, and GNL3L clone formation assays were performed. The final results showed that GNL3L is differentially expressed in the vast majority of cancers, is associated with the prognosis of various cancers, and may affect cancer occurrence through processes such as ribonucleoprotein, ribosomal RNA processing, and cell proliferation. At the same time, it was found that the correlation between GNL3L and TMB, MSI, MMR, and various immune cells is significant. The established ESCA prediction model had a strong predictive ability, and GNL3L could significantly affect the proliferation of esophageal cancer cells. In conclusion, GNL3L may serve as an important prognostic biomarker and play an immunomodulatory role in tumors