A preliminary study on the formation conditions and weathering leaching enrichment mechanism of secondary phosphorite in the Xinhua phosphate mining area, Zhijin, Guizhou

The Xinhua phosphate mining are alocated in Zhijin County, Guizhou Province, is a famous superlarge low-grade phosphate deposit containing rare earth elements in the southwest of China.The average P2O5 grade of the deposit is 17.22%.The proven phosphorite ore resources are 1.348 billion tons, and the rare earth resources are 3 500 kilotons.In recent years, experts and scholars have found that the fluctuation of Xinhua phosphorite ore grade is closely related to weathering, leaching and enrichment effects.To further clarify the influence of weathering and leaching on the element geochemistry as well as the secondary enrichment of phosphate rock, the author focuses on the Gezhongwu ore block and carries out field observations and descriptions of phosphate rock series.A total of 19 chemical analysis samples were collected using block knocking method and 16 rock ore samples were identified through polarizing microscope.On these basis, identification and comprehensive research on the samples were conducted.The results show that the contents of P2O5 in the weathered phosphate rock are 8%-18%, higher than that in the primary phosphate rock, while the contents of MgO are 4%-7%, lower than that in the primary phosphate rock.The weathering intensity of phosphate rock is in the weak to mature stage.The formation of Xinhua weathered phosphate rock in Zhijin County is controlled by lithology, geological structure and hydrological conditions.The purpose of this paper is to provide new information for the further study of the influences of the secondary weathering mineralization of the ore deposit on the chemical composition of phosphate rock, to enrich the metallogenic theories of weathering eluvial phosphate mining in China and to provide the oretical guidance for the rational development and utilization of weathering phosphate resources in the mining area

Pallidal activities during sleep and sleep decoding in dystonia, Huntington's, and Parkinson's disease

Background: Sleep disturbances are highly prevalent in movement disorders, potentially due to the malfunctioning of basal ganglia structures. Pallidal deep brain stimulation (DBS) has been widely used for multiple movement disorders and been reported to improve sleep. We aimed to investigate the oscillatory pattern of pallidum during sleep and explore whether pallidal activities can be utilized to differentiate sleep stages, which could pave the way for sleep-aware adaptive DBS. Methods: We directly recorded over 500 h of pallidal local field potentials during sleep from 39 subjects with movement disorders (20 dystonia, 8 Huntington's disease, and 11 Parkinson's disease). Pallidal spectrum and cortical-pallidal coherence were computed and compared across sleep stages. Machine learning approaches were utilized to build sleep decoders for different diseases to classify sleep stages through pallidal oscillatory features. Decoding accuracy was further associated with the spatial localization of the pallidum. Results: Pallidal power spectra and cortical-pallidal coherence were significantly modulated by sleep-stage transitions in three movement disorders. Differences in sleep-related activities between diseases were identified in non-rapid eye movement (NREM) and REM sleep. Machine learning models using pallidal oscillatory features can decode sleep-wake states with over 90% accuracy. Decoding accuracies were higher in recording sites within the internus-pallidum than the external-pallidum, and can be precited using structural (P < 0.0001) and functional (P < 0.0001) whole-brain neuroimaging connectomics. Conclusion: Our findings revealed strong sleep-stage dependent distinctions in pallidal oscillations in multiple movement disorders. Pallidal oscillatory features were sufficient for sleep stage decoding. These data may facilitate the development of adaptive DBS systems targeting sleep problems that have broad translational prospects

Generalized sleep decoding with basal ganglia signals in multiple movement disorders.

Sleep disturbances profoundly affect the quality of life in individuals with neurological disorders. Closed-loop deep brain stimulation (DBS) holds promise for alleviating sleep symptoms, however, this technique necessitates automated sleep stage decoding from intracranial signals. We leveraged overnight data from 121 patients with movement disorders (Parkinsons disease, Essential Tremor, Dystonia, Essential Tremor, Huntingtons disease, and Tourettes syndrome) in whom synchronized polysomnograms and basal ganglia local field potentials were recorded, to develop a generalized, multi-class, sleep specific decoder - BGOOSE. This generalized model achieved 85% average accuracy across patients and across disease conditions, even in the presence of recordings from different basal ganglia targets. Furthermore, we also investigated the role of electrocorticography on decoding performances and proposed an optimal decoding map, which was shown to facilitate channel selection for optimal model performances. BGOOSE emerges as a powerful tool for generalized sleep decoding, offering exciting potentials for the precision stimulation delivery of DBS and better management of sleep disturbances in movement disorders