Solvent-Dependent Reaction Pathways Operating in Copper(II) Tetrafluoroborate Promoted Oxidative Ring-Opening Reactions of Cyclopropyl Silyl Ethers.

Oxidative ring-opening reactions of benzene-fused bicyclic cyclopropyl silyl ethers, promoted by copper(II) tetrafluoroborate, were investigated. The regioselectivity of cyclopropane ring-opening as well as product distributions were found to be highly dependent on the nature of the solvent. In alcohols, dimeric substances arising from external bond cleavage are major products. Radical rearrangement products are also formed in solvents such as ether and ethyl acetate. On the contrary, nucleophile addition to carbocation intermediates, generated by internal bond cleavage, occurs mainly in reactions taking place in acetonitrile. It is proposed that the observed solvent effects that govern the reaction pathways followed are a consequence of varying solvation of copper intermediates, which governs their reactivity and redox properties. In addition, the influence of counteranions of the copper salts, organonitriles, cyclic dienes, and substrate structures on the pathways followed in these reactions was also examined.

Copper(II)-salt-promoted oxidative ring-opening reactions of bicyclic cyclopropanol derivatives via radical pathways.

Copper(II)-salt-promoted oxidative ring-opening reactions of bicyclic cyclopropanol derivatives were investigated. The regioselectivities of these processes were found to be influenced by the structure of cyclopropanols as well as the counter anion of the copper(II) salts. A mechanism involving rearrangement reactions of radical intermediates and their competitive trapping by copper ions is proposed.

Automatic Detection of Cognitive Impairments through Acoustic Analysis of Speech.

BACKGROUND: Early detection of mild cognitive impairment is crucial in the prevention of Alzheimer's disease. The aim of the present study was to identify whether acoustic features can help differentiate older, independent community-dwelling individuals with cognitive impairment from healthy controls. METHODS: A total of 8779 participants (mean age 74.2 ± 5.7 in the range of 65-96, 3907 males and 4872 females) with different cognitive profiles, namely healthy controls, mild cognitive impairment, global cognitive impairment (defined as a Mini Mental State Examination score of 20-23), and mild cognitive impairment with global cognitive impairment (a combined status of mild cognitive impairment and global cognitive impairment), were evaluated in short-sentence reading tasks, and their acoustic features, including temporal features (such as duration of utterance, number and length of pauses) and spectral features (F0, F1, and F2), were used to build a machine learning model to predict their cognitive impairments. RESULTS: The classification metrics from the healthy controls were evaluated through the area under the receiver operating characteristic curve and were found to be 0.61, 0.67, and 0.77 for mild cognitive impairment, global cognitive impairment, and mild cognitive impairment with global cognitive impairment, respectively. CONCLUSION: Our machine learning model revealed that individuals' acoustic features can be employed to discriminate between healthy controls and those with mild cognitive impairment with global cognitive impairment, which is a more severe form of cognitive impairment compared with mild cognitive impairment or global cognitive impairment alone. It is suggested that language impairment increases in severity with cognitive impairment.