It was found that homoleptic cyclopentadienyl lanthanide complexes CpLn (Ln = Y (1), Yb (2), Sm (3), Nd (4), La (5), Cp = cyclopentadienyl) can be employed as excellent catalysts for the hydroboration of various aldehydes and ketones toward pinacolborane. These robust lanthanide catalysts exhibited high reactivity with low catalyst loadings (0.01-1 mol %) under mild conditions and good functional group tolerability. These complexes also demonstrated uniquely carbonyl-selective hydroboration in the presence of alkenes and alkynes.

Simple and convenient n-BuLi is reported as a highly efficient catalyst in promoting the hydroboration of imines and alkynes.

Homoleptic lanthanide complexes coordinated by a Me-substituted Cp ligand [(MeCp)3Ln] demonstrate unprecedentedly high efficiency in catalyzing the hydroboration of aldehydes and ketones with pinacolborane. This protocol is also applicable for the hydroboration of aryl-substituted imines. In addition, broad functional group compatibility and excellent chemoselectivity is also achieved. DFT calculations are employed to shed light on the reaction mechanism.

The overwhelming spread of Spartina alterniflora (smooth cordgrass) over recent decades has put many native plant communities and coastal environments at risk. Therefore, longterm monitoring of S. alterniflora dynamics is necessary to better understand and manage the invasion of the species. However, it is difficult to map Spartina saltmarshes in China on an annual or multiyear epoch basis. To address this issue, we developed a classification approach integrating Google Earth Engine (GEE) and object-based hierarchical random forest (RF) classification, and we applied this approach to quantify the expansion and dieback of S. alterniflora at Dafeng Milu National Nature Reserve, Jiangsu, China during 1993-2020. Results showed that the area of S. alterniflora expanded from 24.48 ha in 1993 to 1,564.96 ha in 2010. However, after ecological hydrological engineering and an increase in Elaphures davidianus (Pè re David's deer) numbers in 2011, the S. alterniflora area decreased significantly to 944.28 ha in 2020. During 2011-2020, the S. alterniflora area decreased substantially at a rate of 67 ha per year and by 86% in one area studied in Dafeng Milu National Nature Reserve. In 2020, the 944.28 ha of S. alterniflora in the reserve was mainly distributed in mudflats by the sea. Overall, these results show that it is feasible to identify S. alterniflora using the GEE platform and object-based hierarchical RF classification; moreover, this approach could improve understanding and management of this invasion species.

Herein, we present a facile method for deoxygenative hydroboration of a broad range of carboxylic acids under very mild conditions. The most striking feature of this attractive hydroboration is that this elusive and challenging transformation was realized without catalyst and solvent. The investigation of solvent effect showed that tetrahydrofuran was also suitable for this kind of reaction. Moreover, a successful gram-scale trial may provide a very promising toolkit for carboxylic acid reduction at a large scale.

Summary of main observation and conclusion The commercially available homoleptic lanthanum amide La[N(SiMe3)2]3 (LaNTMS) is reported to enable the hydroboration of esters using pinacolborane (HBpin) as the reducing agent. A wide range of substrates including aromatic, aliphatic esters and lactones were applicable to afford corresponding boronic esters in excellent yields under mild and neat conditions with broad functional group compatibility and good chemoselectivity. Furthermore, LaNTMS is capable to realize the very challenging and rarely reported hydroboration of carbonate esters with low catalyst loading at room temperature. Both cyclic and linear carbonate esters can be easily converted to the corresponding products with satisfactory yields. Besides, the hydroboration of alkynes has been developed by using LaNTMS as a catalyst.

Purpose Depression is a common mental illness worldwide and has become an important public health problem. The current clinical diagnosis of depression mainly relies on the doctor’s experience and subjective diagnosis, which results in the low diagnostic efficiency and insufficient objectivity of diagnostic results. Therefore, establishing a physiological and psychological model for computer-aided diagnosis is an urgent task. In order to solve the above problems, this article uses a convolutional neural network (CNN) to identify depression based on electrocardiogram (ECG). Methods Our method uses the raw ECG signal as the input of one-dimensional CNN, and uses the automatic feature processing layer of CNN to learn and distinguish signal features without additional feature extraction and feature selection steps. In order to obtain the optimal model, ECG segments of different durations (3 s, 4 s, 5 s and 6 s) and CNNs with different layers were used for comparison. In order to obtain modeling data, the resting ECG of 37 depression patients and 37 healthy controls were collected. In the proposed network, larger convolution kernels are used to better focus on overall changes. In addition, this article focuses on the inter-patient data classification standard, where the training and test sets come from different patient data. Results Through comprehensive comparison, the 5 s ECG segment and 5-layer CNN are recommended in related applications. The proposed approach achieves high classification performance with accuracy of 93.96%, sensitivity of 89.43%, specificity of 98.49%, positive productivity of 98.34%. Conclusion The experimental results indicate that the end-to-end deep learning approach can identify depression from ECG signals, and possess high diagnostic performance. It also shows that ECG is a potential biomarker in the diagnosis of depression.

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