[Spatiotemporal dynamics of forest cover and its response to climate change in Shandong Province, China during 2000-2022.].

Accurately capturing the spatiotemporal dynamics of regional forest cover and its response to climate change is of great significance for forest resource management and ecological environment protection. We used statistical methods such us linear regression and correlation analysis, as well as remote sensing change monitoring to investigate the spatiotemporal dynamics of forest cover and its response to climate change from 2000 to 2022 in Shandong Province based on MODIS VCF products and meteorological data. The results showed that the forest co-verage and forest area in Shandong Province increased from 43.

Generation and characterization of two acid-resistant macrocin -methyltransferase variants with a higher enzyme activity at 30 °C from .

Tylosin is an important macrolide antibiotic produced by . In the biosynthesis of tylosin, macrocin -methyltransferase TylF catalyzes the conversion of the side-product tylosin C (macrocin) to the primary component tylosin A (C/A conversion). This conversion is the rate-limiting step in the biosynthesis of tylosin, and affects the quality of the end product.

Software Infrastructure for Next-Generation QM/MM-ΔMLP Force Fields.

We present software infrastructure for the design and testing of new quantum mechanical/molecular mechanical and machine-learning potential (QM/MM-ΔMLP) force fields for a wide range of applications. The software integrates Amber's molecular dynamics simulation capabilities with fast, approximate quantum models in the xtb package and machine-learning potential corrections in DeePMD-kit. The xtb package implements the recently developed density-functional tight-binding QM models with multipolar electrostatics and density-dependent dispersion (GFN2-xTB), and the interface with Amber enables their use in periodic boundary QM/MM simulations with linear-scaling QM/MM particle-mesh Ewald electrostatics.

Electronic and Nuclear Quantum Effects on Proton Transfer Reactions of Guanine-Thymine (G-T) Mispairs Using Combined Quantum Mechanical/Molecular Mechanical and Machine Learning Potentials.

Rare tautomeric forms of nucleobases can lead to Watson-Crick-like (WC-like) mispairs in DNA, but the process of proton transfer is fast and difficult to detect experimentally. NMR studies show evidence for the existence of short-time WC-like guanine-thymine (G-T) mispairs; however, the mechanism of proton transfer and the degree to which nuclear quantum effects play a role are unclear. We use a B-DNA helix exhibiting a wGT mispair as a model system to study tautomerization reactions.

Amber free energy tools: Interoperable software for free energy simulations using generalized quantum mechanical/molecular mechanical and machine learning potentials.

We report the development and testing of new integrated cyberinfrastructure for performing free energy simulations with generalized hybrid quantum mechanical/molecular mechanical (QM/MM) and machine learning potentials (MLPs) in Amber. The Sander molecular dynamics program has been extended to leverage fast, density-functional tight-binding models implemented in the DFTB+ and xTB packages, and an interface to the DeePMD-kit software enables the use of MLPs. The software is integrated through application program interfaces that circumvent the need to perform "system calls" and enable the incorporation of long-range Ewald electrostatics into the external software's self-consistent field procedure.

Surface-Accelerated String Method for Locating Minimum Free Energy Paths.

We present a surface-accelerated string method (SASM) to efficiently optimize low-dimensional reaction pathways from the sampling performed with expensive quantum mechanical/molecular mechanical (QM/MM) Hamiltonians. The SASM accelerates the convergence of the path using the aggregate sampling obtained from the current and previous string iterations, whereas approaches like the string method in collective variables (SMCV) or the modified string method in collective variables (MSMCV) update the path only from the sampling obtained from the current iteration. Furthermore, the SASM decouples the number of images used to perform sampling from the number of synthetic images used to represent the path.

Improving rice nitrogen-use efficiency by modulating a novel monouniquitination machinery for optimal root plasticity response to nitrogen.

Plant nitrogen (N)-use efficiency (NUE) is largely determined by the ability of root to take up external N sources, whose availability and distribution in turn trigger the modification of root system architecture (RSA) for N foraging. Therefore, improving N-responsive reshaping of RSA for optimal N absorption is a major target for developing crops with high NUE. In this study, we identified RNR10 (REGULATOR OF N-RESPONSIVE RSA ON CHROMOSOME 10) as the causal gene that underlies the significantly different root developmental plasticity in response to changes in N level exhibited by the indica (Xian) and japonica (Geng) subspecies of rice.

Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states.

Modern semiempirical electronic structure methods have considerable promise in drug discovery as universal "force fields" that can reliably model biological and drug-like molecules, including alternative tautomers and protonation states. Herein, we compare the performance of several neglect of diatomic differential overlap-based semiempirical (MNDO/d, AM1, PM6, PM6-D3H4X, PM7, and ODM2), density-functional tight-binding based (DFTB3, DFTB/ChIMES, GFN1-xTB, and GFN2-xTB) models with pure machine learning potentials (ANI-1x and ANI-2x) and hybrid quantum mechanical/machine learning potentials (AIQM1 and QDπ) for a wide range of data computed at a consistent ωB97X/6-31G* level of theory (as in the ANI-1x database). This data includes conformational energies, intermolecular interactions, tautomers, and protonation states.

QDπ: A Quantum Deep Potential Interaction Model for Drug Discovery.

We report QDπ-v1.0 for modeling the internal energy of drug molecules containing H, C, N, and O atoms. The QDπ model is in the form of a quantum mechanical/machine learning potential correction (QM/Δ-MLP) that uses a fast third-order self-consistent density-functional tight-binding (DFTB3/3OB) model that is corrected to a quantitatively high-level of accuracy through a deep-learning potential (DeepPot-SE).

Growth-Promoting Effects of Zhenqi Granules on Finishing Pigs.

Developing nonantibiotic livestock growth promoters attracts intensive interest in the post-antibiotic era. In this study, we investigated the growth-promoting efficacy of Zhenqi granules (ZQ) in pigs and further explored the possible mechanisms by transcriptomics analysis. Weaned piglets (52 days old with an average body weight of 17.

Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery.

Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows.

Improved Alchemical Free Energy Calculations with Optimized Smoothstep Softcore Potentials.

Progress in the development of GPU-accelerated free energy simulation software has enabled practical applications on complex biological systems and fueled efforts to develop more accurate and robust predictive methods. In particular, this work re-examines concerted (a.k.

Validation of Free Energy Methods in AMBER.

Herein we provide high-precision validation tests of the latest GPU-accelerated free energy code in AMBER. We demonstrate that consistent free energy results are obtained in both the gas phase and in solution. We first show, in the context of thermodynamic integration (TI), that the results are invariant with respect to "split" (e.

Effective flocculation of target microalgae with self-flocculating microalgae induced by pH decrease.

A flocculation method was developed to harvest target microalgae with self-flocculating microalgae induced by decreasing pH to just below isoelectric point. The flocculation efficiencies of target microalgae were much higher than those flocculated only via pH decrease. The mechanism could be that negatively charged self-flocculating microalgal cells became positively charged during pH decrease, subsequently attracted negatively charged target microalgae cells to form flocs and settled down due to gravity.

Freshwater microalgae harvested via flocculation induced by pH decrease.

Background: Recent studies have demonstrated that microalga has been widely regarded as one of the most promising raw materials of biofuels. However, lack of an economical, efficient and convenient method to harvest microalgae is a bottleneck to boost their full-scale application. Many methods of harvesting microalgae, including mechanical, electrical, biological and chemical based, have been studied to overcome this hurdle.