Biomimetic iron and manganese complexes have emerged as important catalysts in chemo-, regio-, and stereoselective oxidation reactions. In this study, we describe a remote hydroxylation of undirected C(sp3)–H bonds utilizing a simple manganese complex as a catalyst and hydrogen peroxide (H2O2) as a terminal oxidant in the presence of bromoacetic acid (BrCH2CO2H) as an additive. Crucial features of this catalytic system are the excellent catalytic activity of an easily preparable manganese catalyst, [Mn(R,R-BPMCN)]2+ (1), a low catalyst loading, a short reaction time, a broad substrate scope, and an easy scale-up. Mechanistic studies were also performed to elucidate the role of BrCH2CO2H and the nature of the hydroxylating intermediate, revealing that the BrCH2CO2H additive facilitates the generation of a highly electrophilic Mn(V)–oxo bromoacetate intermediate as a responsible oxidant via a heterolytic O–O bond cleavage of a postulated Mn(III)–OOH precursor. One notable observation in the mechanistic studies was that a significant amount of 18O was incorporated from H218O into the alcohol product in these catalytic oxidation reactions. On the basis of the above experimental observations and from the support of density functional theory (DFT) calculations, we conclude that a highly electrophilic Mn(V)–oxo bromoacetate complex was generated as a responsible oxidant that effects the undirected C(sp3)–H hydroxylation via an oxygen-rebound mechanism, thus mimicking both the structure and the function of the active intermediate of iron(IV)–oxo succinate for α-ketoglutarate (αKG)-dependent nonheme iron oxygenases. 

Bromoacetic Acid-Promoted Nonheme Manganese-Catalyzed Alkane Hydroxylation Inspired by α-Ketoglutarate-Dependent Oxygenases

Designing catalytic approaches to deliberate and selective C–H activation was listed by Bergman among the "Holy Grails" of synthetic chemistry in 1995, and continues to be a hot topic up to now, mostly in the context of late-stage functionalization of complex molecular structures ( Acc. Chem. Res. 1995, 28, 154−162, DOI: 10.1021/ar00051a009). This contribution surveys the asymmetric oxygenations of aliphatic C–H groups of organic molecules that have been reported to date, including enantio- and diastereoselective hydroxylations at prochiral C–H groups and oxidative desymmetrizations (through either ketonization or hydroxylation), via direct creation of new C–O bonds with the aid of synthetic transition metal based catalysts. Catalytic stereoselective C(sp3)–H oxygenations of complex structures of relevance for developing synthetic approaches for late-stage functionalization of bioactive molecules (such as, mostly, steroids and terpenoids) for drug discovery and development are considered. Synthetic aspects of the oxidative transformations as above are the major focus of the review. The mechanistic bases of the above processes are discussed briefly, in connection to the factors that govern the oxidation regio-, chemo-, and stereoselectivity. 

Transition Metal-Catalyzed Direct Stereoselective Oxygenations of C(sp3)–H Groups

A Review of Graph Theory-Based Diagnosis of Neurological Disorders Based on EEG and MRI

MRI and PET are crucial diagnostic tools for brain diseases, as they provide complementary information on brain structure and function. However, PET scanning is costly and involves radioactive exposure, resulting in a lack of PET. Moreover, simultaneous PET and MRI at ultra-high-field are currently hardly infeasible. Ultra-high-field imaging has unquestionably proven valuable in both clinical and academic settings, especially in the field of cognitive neuroimaging. These motivate us to propose a method for synthetic PET from high-filed MRI and ultra-high-field MRI. From a statistical perspective, the joint probability distribution (JPD) is the most direct and fundamental means of portraying the correlation between PET and MRI. This paper proposes a novel joint diffusion attention model which has the joint probability distribution and attention strategy, named JDAM. JDAM has a diffusion process and a sampling process. The diffusion process involves the gradual diffusion of PET to Gaussian noise by adding Gaussian noise, while MRI remains fixed. JPD of MRI and noise-added PET was learned in the diffusion process. The sampling process is a predictor-corrector. PET images were generated from MRI by JPD of MRI and noise-added PET. The predictor is a reverse diffusion process and the corrector is Langevin dynamics. Experimental results on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed method outperforms state-of-the-art CycleGAN for high-field MRI (3T MRI). Finally, synthetic PET images from the ultra-high-field (5T MRI and 7T MRI) be attempted, providing a possibility for ultra-high-field PET-MRI imaging.

Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model

The titanium complex of the cis-1,2-diaminocyclohexane (cis-DACH) derived Berkessel-salalen ligand is a highly efficient and enantioselective catalyst for the asymmetric epoxidation of terminal olefins with hydrogen peroxide ("Berkessel-Katsuki catalyst"). We herein report that this epoxidation catalyst also effects the highly enantioselective hydroxylation of benzylic C-H bonds with hydrogen peroxide. Mechanism-based ligand optimization identified a novel nitro-salalen Ti-catalyst of the highest efficiency ever reported for asymmetric catalytic benzylic hydroxylation, with enantioselectivities of up to 98% ee, while overoxidation to ketone is marginal. The novel nitro-salalen Ti-catalyst also shows enhanced epoxidation ef­ficiency, as evidenced by e.g. the conversion of 1-decene to its ep­oxide in 90% yield with 94% ee, at a catalyst loading of 0.1 mol-% only. 

Titanium Salalen Catalyzed Enantioselective Benzylic Hydroxylation

https://pubs.rsc.org/en/content/articlepdf/2021/cy/d1cy01642c

Exploration of Highly Electron-Rich Manganese Complexes in Enantioselective Oxidation Catalysis; a Focus on Enantioselective Benzylic Oxidation

Following the aging of the population, Parkinson's disease (PD) poses a severe challenge to public health. For the diagnosis of PD and the prediction of its progression, numerous computer-aided diagnosis procedures have been developed. Recently, Graph Convolutional Networks (GCN) are widely applied in deep learning to effectively integrate multi-modal features and model subject correlation. However, many GCNs which are used for node classification build large-scale fixed graph topologies using the entire dataset, which could make them impossible to verify independently. Furthermore, past GCN algorithms would need more interpretability, limiting their real-world applications. In this paper, an Interpretable Graph-Learning Convolutional Network (iGLCN) is proposed to enhance the performance of personalized diagnosis for PD while simultaneously producing interpretable results. The proposed method can dynamically adjust the graph structure for GCN to better diagnose outcomes by learning the optimal underlying latent graph. Through interpretable feature learning, the proposed network can interpret diagnosis outcomes. The experiments showed that the proposed method increased flexibility while maintaining a high level of classification performance and could be interpretable for PD diagnosis.Clinical Relevance— The proposed method is expected to have good performance in its strong practicability, feasibility, and interpretability for Parkinson’s disease diagnosis.

Developing a Dynamic Graph Network for Interpretable Analysis of Multi-Modal MRI Data in Parkinson’s Disease Diagnosis

Correction of B0 Eddy Current Effects in Magnetic Resonance Fingerprinting at 5.0T Whole Body Scanner

In this paper, a dynamic dual-graph fusion convolutional network is proposed to improve Alzheimer’s disease (AD) diagnosis performance. The following are the paper’s main contributions: (a) propose a novel dynamic Graph Convolutional Network (GCN) architecture, which is an end-to-end pipeline for diagnosis of the AD task; (b) the proposed architecture can dynamically adjust the graph structure for GCN to produce better diagnosis outcomes by learning the optimal underlying latent graph; (c) incorporate feature graph learning and dynamic graph learning, giving those useful features of subjects more weight while decreasing the weights of other noise features. Experiments indicate that our model provides flexibility and stability while achieving excellent classification results in AD diagnosis.

pdf/dynamic-dual-graph-fusion-convolutional-network-for-375nutfn5t.pdf

Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis

MRI and PET are important modalities and can provide complementary information for the diagnosis of brain diseases because MRI can provide structural information of brain and PET can obtain functional information of brain. However, PET is usually missing. Especially, simultaneous PET and MRI imaging at ultrahigh field is not achievable in the current. Thus, synthetic PET using MRI at ultrahigh field is essential. In this paper, we synthetic PET using MRI as a guide by joint probability distribution of diffusion model (JPDDM). Meanwhile, We utilized our model in Ultrahigh Fields.

Brain PET Synthesis from MRI Using Joint Probability Distribution of Diffusion Model at Ultrahigh Fields

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