Giant room temperature anomalous Hall effect and magnetically tuned topology in the ferromagnetic Weyl semimetal Co2MnAl

Weyl semimetals (WSM) have been extensively studied due to their exotic properties such as topological surface states and anomalous transport phenomena. Their band structure topology is usually predetermined by material parameters and can hardly be manipulated once the material is formed. Their unique transport properties appear usually at very low temperature, which sets challenges for practical device applications. In this work, we demonstrate a way to modify the band topology via a weak magnetic field in a ferromagnetic topological semimetal, Co2MnAl, at room temperature. We observe a tunable, giant anomalous Hall effect, which is induced by the transition between Weyl points and nodal rings as rotating the magnetization axis. The anomalous Hall conductivity is as large as that of a 3D quantum anomalous Hall effect (QAHE), with the Hall angle reaching a record value (21%) at the room temperature among magnetic conductors. Furthermore, we propose a material recipe to generate the giant anomalous Hall effect by gaping nodal rings without requiring the existence of Weyl points. Our work reveals an ideal intrinsically magnetic platform to explore the interplay between magnetic dynamics and topological physics for the development of a new generation of spintronic devices.Comment: 4 figures, 8 pages for the main text. The supplementary materials are included to

General considerations of model-based meta-analysis

With the increasing cost of drug development and clinical trials, it is of great value to make full use of all kinds of data to improve the efficiency of drug development and to provide valid information for medication guidelines. Model-based meta-analysis (MBMA) combines mathematical models with meta-analysis to integrate information from multiple sources (preclinical and clinical data, etc.) and multiple dimensions (targets/mechanisms, pharmacokinetics/pharmacodynamics, diseases/indications, populations, regimens, biomarkers/efficacy/safety, etc.), which not only provides decision-making for all key points of drug development, but also provides effective information for rational drug use and cost-effectiveness analysis. The classical meta-analysis requires high homogeneity of the data, while MBMA can combine and analyze the heterogeneous data of different doses, different time courses, and different populations through modeling, so as to quantify the dose-effect relationship, time-effect relationship, and the relevant impact factors, and thus the efficacy or safety features at the level of dose, time and covariable that have not been involved in previous studies. Although the modeling and simulation methods of MBMA are similar to population pharmacokinetics/pharmacodynamics (Pop PK/PD), compared with Pop PK/PD, the advantage of MBMA is that it can make full use of literature data, which not only improves the strength of evidence, but also can answer the questions that have not been proved or can not be answered by a single study. At present, MBMA has become one of the important methods in the strategy of model-informed drug development (MIDD). This paper will focus on the application value, data analysis plan, data acquisition and processing, data analysis and reporting of MBMA, in order to provide reference for the application of MBMA in drug development and clinical practice

Quantitative analysis of efficacy and safety of LABA/LAMA fixed-dose combinations in the treatment of stable COPD

Objective: This study aimed to quantitatively compare the efficacy and safety of long-acting β2-agonist (LABA)/long-acting muscarinic antagonist (LAMA) fixed-dose combinations (FDCs) for the treatment of stable chronic obstructive pulmonary disease (COPD), especially in terms of their loss of efficacy in lung function. Methods: Randomized controlled clinical trials of LABA/LAMA FDCs for the treatment of stable COPD were comprehensively searched for in public databases. Pharmacodynamic models were established to describe the time course of the primary outcome [trough forced expiratory volume in the first second (FEV 1 )]. Secondary outcomes [COPD exacerbations, St. George’s Respiratory Questionnaire (SGRQ), Transition Dyspnoea Index (TDI), and rescue medication use] and safety outcomes [mortality, serious adverse events (SAEs), and withdrawals due to adverse events (AEs)] were also compared via a meta-analysis. Results: A total of 22 studies involving 16,486 participants were included in this study. The results showed that in terms of primary outcome (change from baseline in trough FEV 1 ), the efficacy of vilanterol/umeclidinium was the highest, while the efficacy of formoterol/aclidinium was the lowest, with a maximum effect value (E max ) of 0.185 L [95% confidence interval (CI): 0.173–0.197 L] and 0.119 L (95% CI: 0.103–0.135 L), respectively. The efficacy of other drugs, such as formoterol/glycopyrronium, indacaterol/glycopyrronium, and olodaterol/tiotropium, were comparable, and their E max values were 0.150–0.177 L. Except for vilanterol/umeclidinium, the other four LABA/LAMA FDCs showed a certain degree of loss of efficacy. Compared with the efficacy at 2 days, the trough FEV 1 (L) relative to baseline at 24 weeks decreased by 0.029–0.041 L. In terms of secondary outcomes, the efficacy of different LABA/LAMA FDCs was similar in TDI and rescue medication use. However, formoterol/aclidinium was better in preventing the COPD exacerbations, while vilanterol/umeclidinium was the best in terms of SGRQ. In addition, different LABA/LAMA FDCs and placebo had similar safety outcomes. Conclusion: The present findings may provide necessary quantitative information for COPD medication guidelines

Optimizing the Spatial Structure of Metasequoia Plantation Forest Based on UAV-LiDAR and Backpack-LiDAR

Optimizing the spatial structure of forests is important for improving the quality of forest ecosystems. Light detection and ranging (LiDAR) could accurately extract forest spatial structural parameters, which has significant advantages in spatial optimization and resource monitoring. In this study, we used unmanned aerial vehicle LiDAR (UAV-LiDAR) and backpack-LiDAR to acquire point cloud data of Metasequoia plantation forests from different perspectives. Then the parameters, such as diameter at breast height and tree height, were extracted based on the point cloud data, while the accuracy was verified using ground-truth data. Finally, a single-tree-level thinning tool was developed to optimize the spatial structure of the stand based on multi-objective planning and the Monte Carlo algorithm. The results of the study showed that the accuracy of LiDAR-based extraction was (R2 = 0.96, RMSE = 3.09 cm) for diameter at breast height, and the accuracy of R2 and RMSE for tree height extraction were 0.85 and 0.92 m, respectively. Thinning improved stand objective function value Q by 25.40%, with the most significant improvement in competition index CI and openness K of 17.65% and 22.22%, respectively, compared to the pre-optimization period. The direct effects of each spatial structure parameter on the objective function values were ranked as follows: openness K (1.18) > aggregation index R (0.67) > competition index CI (0.42) > diameter at breast height size ratio U (0.06). Additionally, the indirect effects were ranked as follows: aggregation index R (0.86) > diameter at breast height size ratio U (0.48) > competition index CI (0.33). The study realized the optimization of stand spatial structure based on double LiDAR data, providing a new reference for forest management and structure optimization

Optimization of secukinumab dose regimens in patients with moderate-to-severe plaque psoriasis via exposure-response modeling

Further dose optimization is required for patients with moderate-to-severe plaque psoriasis who do not benefit from the approved secukinumab dose regimen. This study aimed to develop an exposure-response model for secukinumab to recommend dose regimens for patients of different body weights. We searched the PubMed and Cochrane Library databases for randomized controlled trials using PASI 75 and PASI 90 response rates as primary outcomes. A model-based meta-analysis was developed to quantitatively analyze the distribution of six secukinumab dose regimens in patients weighing 50–120 kg. Sixteen trials involving 6,197 subjects were included in the analysis. The established model accurately described the time-course characteristics of PASI 75 and PASI 90 response rates over 52 weeks. Simulations indicated that maintenance doses could be reduced to 150 mg every 4 weeks and to 150 mg every 3 weeks for patients weighing 50 and 60 kg, respectively. In contrast, maintenance doses of 300 mg every 3 weeks should be selected for patients weighing 120 kg. Patients weighing 70–110 kg remained on approved maintenance doses of 300 mg every 4 weeks. Based on patient body weights, the exposure-response model recommends efficacious and economical dose regimens for patients with moderate-to-severe plaque psoriasis.</p

UAV-LiDAR Integration with Sentinel-2 Enhances Precision in AGB Estimation for Bamboo Forests

Moso bamboo forests, recognized as a distinctive and significant forest resource in subtropical China, contribute substantially to efficient carbon sequestration. The accurate assessment of the aboveground biomass (AGB) in Moso bamboo forests is crucial for evaluating their impact on the carbon balance within forest ecosystems at a regional scale. In this study, we focused on the Moso bamboo forest located in Shanchuan Township, Zhejiang Province, China. The primary objective was to utilize various data sources, namely UAV-LiDAR (UL), Sentinel-2 (ST), and a combination of UAV-LiDAR with Sentinel-2 (UL + ST). Employing the Boruta algorithm, we carefully selected characterization variables for analysis. Our investigation delved into establishing correlations between UAV-LiDAR characterization parameters, Sentinel-2 feature parameters, and the aboveground biomass (AGB) of the Moso bamboo forest. Ground survey data on Moso bamboo forest biomass served as the basis for our analysis. To enhance the accuracy of AGB estimation in the Moso bamboo forest, we employed three distinct modeling techniques: multivariate linear regression (MLR), support vector regression (SVR), and random forest (RF). Through this approach, we aimed to compare the impact of different data sources and modeling methods on the precision of AGB estimation in the studied bamboo forest. This study revealed that (1) the point cloud intensity of UL, the variables of canopy cover (CC), gap fraction (GF), and leaf area index (LAI) reflect the structure of Moso bamboo forests, and the variables indicating the height of the forest stand (AIH1, AIHiq, and Hiq) had a significant effect on the AGB of Moso bamboo forests, significantly impact Moso bamboo forest AGB. Vegetation indices such as DVI and SAVI in ST also exert a considerable effect on Moso bamboo forest AGB. (2) AGB estimation models constructed based on UL consistently demonstrated higher accuracy compared with ST, achieving R2 values exceeding 0.7. Regardless of the model used, UL consistently delivered superior accuracy in Moso bamboo forest AGB estimation, with RF achieving the highest precision at R2 = 0.88. (3) Integration of ST with UL substantially improved the accuracy of AGB estimation for Moso bamboo forests across all three models. Specifically, using RF, the accuracy of AGB estimation increased by 97.7%, with R2 reaching 0.89 and RMSE reduced by 124.4%. As a result, the incorporation of LiDAR data, which reflects the stand structure, has proven to enhance the accuracy of aboveground biomass (AGB) estimation in Moso bamboo forests when combined with multispectral remote sensing data. This integration serves as an effective solution to address the limitations of single optical remote sensing methods, which often suffer from signal saturation, leading to lower accuracy in estimating Moso bamboo forest biomass. This approach offers a novel perspective and opens up new possibilities for improving the precision of Moso bamboo forest biomass estimation through the utilization of multiple remote sensing sources

Bipolaronic Nature of the Pseudogap in Quasi-One-Dimensional (TaSe4_4)2_2IRevealed via Weak Photoexcitation

The origin of the pseudogap in many strongly correlated materials has been a longstanding puzzle. Here, we present experimental evidence that many-body interactions among small Holstein polarons, i.e., the formation of bipolarons, are primarily responsible for the pseudogap in (TaSe$_4$)$_2$I. After weak photoexcitation of the material, we observe the appearance of both dispersive (single-particle bare band) and flat bands (single-polaron sub-bands) in the gap by using time- and angle-resolved photoemission spectroscopy. Based on Monte Carlo simulations of the Holstein model, we propose that the melting of pseudogap and emergence of new bands originate from a bipolaron to single-polaron crossover. We also observe dramatically different relaxation times for the excited in-gap states in (TaSe$_4$)$_2$I (∼600 fs) compared with another 1D material Rb$_{0.3}$MoO$_3$ (∼60 fs), which provides a new method for distinguishing between pseudogaps induced by polaronic or Luttinger-liquid many-body interactions