Explore the vast range of titles available.

Precise fundamental atmospheric stellar parameters and abundance determination of individual elements in stars are important for all stellar population studies. Non–local thermodynamic equilibrium (non-LTE; hereafter NLTE) models are often important for such high precision, however, can be computationally complex and expensive, which renders the models less utilized in spectroscopic analyses. To alleviate the computational burden of such models, we developed a robust 1D, NLTE fundamental atmospheric stellar parameter derivation tool, LOTUS, to determine the effective temperature T eff, surface gravity logg , metallicity [Fe/H], and microturbulent velocity v mic for FGK-type stars, from equivalent width (EW) measurements of Fe i and Fe ii lines. We utilize a generalized curve of growth method to take into account the EW dependencies of each Fe i and Fe ii line on the corresponding atmospheric stellar parameters. A global differential evolution optimization algorithm is then used to derive the fundamental parameters. Additionally, LOTUS can determine precise uncertainties for each stellar parameter using a Markov Chain Monte Carlo algorithm. We test and apply LOTUS on a sample of benchmark stars, as well as stars with available asteroseismic surface gravities from the K2 survey, and metal-poor stars from the Gaia-ESO and R-Process Alliance surveys. We find very good agreement between our NLTE-derived parameters in LOTUS to nonspectroscopic values on average within T eff = ±30 K, and logg = ±0.10 dex for benchmark stars. We provide open access of our code, as well as of the interpolated precomputed NLTE EW grids available on Github (the software is available on GitHub 3 3 https://github.com/Li-Yangyang/LOTUS under an MIT License, and version 0.1.1 (as the persistent version) is archived in Zenodo) and documentation with working examples on the Readthedocs book.

The precise separation of multicomponent signals encounters numerous challenges due to the complexity of signals and widespread interference. Synchrosqueezing Transform (SST) is one of the important technologies for improving the accurate separation of multicomponent signals, but it faces challenges in terms of the difficulty and effectiveness of squeezing. This paper introduces a multicomponent signal separation method based on innovative Fractional Synchrosqueezing Transform (FrSST). FrSST rearranges along the fractional frequency axis, improving the accuracy of time–frequency ridges and, consequently, enhancing the precision of multicomponent signal separation. In the signal reconstruction process, chirp multiplication and energy rearrangement compensate for chirp bases’ effects, boosting energy concentration and reconstruction potential. Utilizing improved ridges from FrSST ensures effective signal reconstruction. Simulation comparisons demonstrate that, with varying SNRs from − 5 to 15 dB, the reconstructed components based on FrSST exhibit favorable approximation to the original signal components. Furthermore, as the sample size increases, the proposed algorithm shows satisfactory computational efficiency.

Background With the surge in genomic data driven by advancements in sequencing technologies, the demand for efficient bioinformatics tools for sequence analysis has become paramount. BLAST-like alignment tool (BLAT), a sequence alignment tool, faces limitations in performance efficiency and integration with modern programming environments, particularly Python. This study introduces PxBLAT, a Python-based framework designed to enhance the capabilities of BLAT, focusing on usability, computational efficiency, and seamless integration within the Python ecosystem. Results PxBLAT demonstrates significant improvements over BLAT in execution speed and data handling, as evidenced by comprehensive benchmarks conducted across various sample groups ranging from 50 to 600 samples. These experiments highlight a notable speedup, reducing execution time compared to BLAT. The framework also introduces user-friendly features such as improved server management, data conversion utilities, and shell completion, enhancing the overall user experience. Additionally, the provision of extensive documentation and comprehensive testing supports community engagement and facilitates the adoption of PxBLAT. Conclusions PxBLAT stands out as a robust alternative to BLAT, offering performance and user interaction enhancements. Its development underscores the potential for modern programming languages to improve bioinformatics tools, aligning with the needs of contemporary genomic research. By providing a more efficient, user-friendly tool, PxBLAT has the potential to impact genomic data analysis workflows, supporting faster and more accurate sequence analysis in a Python environment.

Post-stroke depression is a common complication of stroke, with a high incidence rate and low recognition rate. Many patients do not receive effective intervention at the onset, which affects subsequent treatment outcomes. Post-stroke depression not only impacts the patient's mental well-being but also increases the risk of stroke recurrence and poor prognosis. Therefore, it has become a significant public health concern. Acupuncture has gained significant popularity in the treatment of post-stroke depression. However, there are inconsistent clinical research results regarding its efficacy and safety. This systematic review aims to gather and critically assess all available evidence regarding the effectiveness and safety of acupuncture in the treatment of post-stroke depression in patients. We will conduct thorough searches for relevant studies in multiple electronic databases (PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, VIP Database, Wan-fang Data and China Biomedical Database). Our search scope will encompass studies published from the inception of each database until September 2023. To evaluate the potential bias in all the included studies, we will adhere to the guidelines offered in the Cochrane Handbook. The total effective rate will be the primary outcome. To conduct a systematic review, we will employ RevMan 5.4 software. This study will obtain efficacy and safety of acupuncture for the treatment of post-stroke depression. The conclusions of this study will provide evidence-based perspectives that can guide clinical decision-making regarding the practicality and recommended timing of using acupuncture to treat post-stroke depression. Furthermore, this study will help advance the clinical application of acupuncture treatment for post-stroke depression and enhance its efficacy while ensuring patient safety.

Accurate prediction of enzyme kinetic parameters is crucial for enzyme exploration and modification. Existing models face the problem of either low accuracy or poor generalization ability due to overfitting. In this work, we first developed unbiased datasets to evaluate the actual performance of these methods and proposed a deep learning model, CataPro, based on pre-trained models and molecular fingerprints to predict turnover number ( k c a t ), Michaelis constant ( K m ), and catalytic efficiency ( k c a t / K m ). Compared with previous baseline models, CataPro demonstrates clearly enhanced accuracy and generalization ability on the unbiased datasets. In a representational enzyme mining project, by combining CataPro with traditional methods, we identified an enzyme (SsCSO) with 19.53 times increased activity compared to the initial enzyme (CSO2) and then successfully engineered it to improve its activity by 3.34 times. This reveals the high potential of CataPro as an effective tool for future enzyme discovery and modification. Enzyme kinetic parameter prediction is a challenge in enzyme discovery and engineering. Here, the authors train a robust deep learning model CataPro to predict enzyme kinetic parameters and validate its practicality through wet-lab experiments.

Synthesis of ethanol from non-petroleum carbon resources via syngas (a mixture of H 2 and CO) is an important but challenging research target. The current conversion of syngas to ethanol suffers from low selectivity or multiple processes with high energy consumption. Here, we report a high-selective conversion of syngas into ethanol by a triple tandem catalysis. An efficient trifunctional tandem system composed of potassium-modified ZnO–ZrO 2 , modified zeolite mordenite and Pt–Sn/SiC working compatibly in syngas stream in one reactor can afford ethanol with a selectivity of 90%. We demonstrate that the K + –ZnO–ZrO 2 catalyses syngas conversion to methanol and the mordenite with eight-membered ring channels functions for methanol carbonylation to acetic acid, which is then hydrogenated to ethanol over the Pt–Sn/SiC catalyst. The present work offers an effective methodology leading to high selective conversion by decoupling a single-catalyst-based complicated and uncontrollable reaction into well-controlled multi-steps in tandem in one reactor. Direct synthesis of ethanol from non-petroleum carbon resources via syngas (CO/H 2 ) is a highly attractive but challenging target. Here, the authors report a triple tandem catalytic system for single-pass conversion of syngas into ethanol with selectivity as high as 90%.

Glacial–interglacial changes in the distribution of C₃/C₄ vegetation on the Chinese Loess Plateau have been related to East Asian summer monsoon intensity and position, and could provide insights into future changes caused by global warming. Here, we present δ13C records of bulk organic matter since the Last Glacial Maximum (LGM) from 21 loess sections across the Loess Plateau. The δ13C values (range: −25‰ to −16‰) increased gradually both from the LGM to the mid-Holocene in each section and from northwest to southeast in each time interval. During the LGM, C₄ biomass increased from <5% in the northwest to 10–20% in the southeast, while during the mid-Holocene C₄ vegetation increased throughout the Plateau, with estimated biomass increasing from 10% to 20% in the northwest to >40% in the southeast. The spatial pattern of C₄ biomass in both the LGM and the mid-Holocene closely resembles that of modern warm-season precipitation, and thus can serve as a robust analog for the contemporary East Asian summer monsoon rain belt. Using the 10–20% isolines for C₄ biomass in the cold LGM as a reference, we derived a minimum 300-km northwestward migration of the monsoon rain belt for the warm Holocene. Our results strongly support the prediction that Earth’s thermal equator will move northward in a warmer world. The southward displacement of the monsoon rain belt and the drying trend observed during the last few decades in northern China will soon reverse as global warming continues.

Object detection has made significant progress in many real-world scenes. Despite this remarkable progress, the common use case of detection in remote sensing images remains challenging even for leading object detectors, due to the complex background, objects with arbitrary orientation, and large difference in scale of objects. In this paper, we propose a novel rotation detector for remote sensing images, mainly inspired by Mask R-CNN, namely RADet. RADet can obtain the rotation bounding box of objects with shape mask predicted by the mask branch, which is a novel, simple and effective way to get the rotation bounding box of objects. Specifically, a refine feature pyramid network is devised with an improved building block constructing top-down feature maps, to solve the problem of large difference in scales. Meanwhile, the position attention network and the channel attention network are jointly explored by modeling the spatial position dependence between global pixels and highlighting the object feature, for detecting small object surrounded by complex background. Extensive experiments on two remote sensing public datasets, DOTA and NWPUVHR -10, show our method to outperform existing leading object detectors in remote sensing field.

Selective hydrogenation of acetylene in excess ethylene is an important reaction in both fundamental study and practical application. Pd-based catalysts with high intrinsic activity are commonly employed, but usually suffer from low selectivity. Pd single-atom catalysts (SACs) usually exhibit outstanding ethylene selectivity due to the weak π-bonding ethylene adsorption. However, the preparation of high-loading and stable Pd SACs is still confronted with a great challenge. In this work, we report a simple strategy to fabricate Pd SACs by means of reducing conventional supported Pd catalysts at suitable temperatures to selectively encapsulate the co-existed Pd nanoparticles (NPs)/clusters. This is based on our new finding that single atoms only manifest strong metal-support interaction (SMSI) at higher reduction temperature than that of NPs/clusters. The derived Pd SACs (Pd 1 /CeO 2 and Pd 1 /α-Fe 2 O 3 ) were applied to acetylene selective hydrogenation, exhibiting much improved ethylene selectivity and high stability. This work offers a promising way to develop stable Pd SACs easily.

Abiotic stress such as cold, drought, saline-alkali stress and biotic stress including disease and insect pest are the main factors that affect plant growth and limit agricultural productivity. In recent years, with the rapid development of molecular biology, genome editing techniques have been widely used in botany and agronomy due to their characteristics of high efficiency, controllable and directional editing. Genome editing techniques have great application potential in breeding resistant varieties. These techniques have achieved remarkable results in resistance breeding of important cereal crops (such as maize, rice, wheat, etc.), vegetable and fruit crops. Among them, CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated) provides a guarantee for the stability of crop yield worldwide. In this paper, the development of CRISRR/Cas and its application in different resistance breeding of important crops are reviewed, the advantages and importance of CRISRR/Cas technology in breeding are emphasized, and the possible problems are pointed out.