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Winter precipitation types (WPTs) are controlled by many factors, including thermodynamic and microphysical processes. Therefore, realistically simulating interactions between precipitation particles and the atmosphere is important when diagnosing the WPT. In the present study, we analyze the performance of the one-dimensional spectral bin model (SBM) developed by Carlin and Ryzhkov (2019), which simulates the change in the physical characteristics of precipitation particles of various sizes as they fall from the cloud top to the ground and diagnoses surface WPT. We compare the performance of the SBM and four other diagnostic methods that use the following variables: 1) atmospheric thickness, 2) wet-bulb temperature, 3) temperature and relative humidity, and 4) wet-bulb temperature and low-level lapse rate. Three reference WPTs (snow [SN], rain [RA], and RASN) are obtained from particle size velocity (PARSIVEL) disdrometer data using a newly proposed decision algorithm. The results show that the SBM has the highest overall skill score for winter precipitation, especially at the mountain sites. In contrast, the skill score of the SBM is lower than the other methods for RA. These results indicate that the SBM simulations tend to underestimate melting compared to observations. We thus explore the effects of the SBM's microphysics scheme on the extent of melting in cases of misdiagnosed RA. An optimized SBM that uses the climatological snow density‑diameter relationship for the Pyeongchang region produces an increased amount of melting and achieves an improved skill score compared to the original SBM, which uses climatological relationships for Colorado region. [ABSTRACT FROM AUTHOR]

ABSTRACT There is a lack of methodological investigation of the in situ functions of bacterial species in microecosystems. Here, we used native phages as a microbial editing tool for eliminating Escherichia coli strain MG1655 labeled with green fluorescent protein (GFP) in the mouse gut. The virulent phages (W1 and W3) possessed host specificity at both the genus and species levels, resulting in an 8.8-log10 difference in the titer of viable bacteria after 12 h of phage treatment compared with that in the phage-free control in an in vitro test. In vivo, they reduced strain MG1655 colonizing the mouse gut at concentrations of 106 to 108 CFU g−1 to a 102 CFU g−1 level, which is almost undetectable by the plate colony-counting method. Moreover, the impact of phage treatment on the microbial community structure of the mouse gut was not significant (P > 0.05), indicating that native phages can effectively edit a target bacterium, with limited perturbation of microbial diversity and relative abundance. Therefore, we developed an engineering technique for investigation of the functions of a specific bacterium by depleting its abundance in microecosystems. IMPORTANCE This report describes a gut engineering technique for investigation of the functions of a specific bacterium. Native phages with host specificity can knock down the corresponding E. coli strain in the mouse gut with limited perturbation of microbial diversity and relative abundance, indicating that they, as a microbial editing tool, can effectively edit the abundance of a target bacterium. Such an approach is undoubtedly of interest in the context of lack of knowledge of how to methodologically study the in situ function of a specific species in a complex microecosystem.

A palladium‐catalyzed asymmetric 1,n‐remote aminoacetoxylation of cis‐alkenes has been developed using PhI(OAc)2 as an oxidant, providing the acetoxylated lactams with excellent enantioselectivities under mild reaction conditions. The sterically hindered pyridine‐oxazoline (Pyox) L3 with a tert‐butyl group in oxazoline ring and propyl group in C6 position of pyridinyl is vital for the reaction, where the former is good for asymmetric aminopalladation step and the latter for the chain walking process. The enantioenriched lactam products were proven to be good building blocks for the synthesis of azabicycles. [ABSTRACT FROM AUTHOR]

Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (M ulti- S ource I nformation-based D rug- T arget I nteraction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a D rug- T arget K nowledge G raph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https://github.com/KEAML-JLU/MSI-DTI. [ABSTRACT FROM AUTHOR]