Overview of Gemstone Resources in China

Gemstones are minerals of gem qualities used for adornment and decoration with the attributes of beauty, durability and rarity. Traditionally, although China has been regarded as the most important source for nephrite, over the past decades, a large variety of gemstone resources have been newly discovered in China owing to continuous exploration works. The vast land with various geological and geochemical backgrounds is rich in gemstone resources with potential for new deposits discoveries. In pegmatites, gemstones are related to granitic magma events and mainly occur in pegmatitic cavities, such as tourmaline, aquamarine, spodumene, spessartine, moonstone, quartz, apatite, and topaz. The eruption of Tertiary basaltic magma provides gem-quality sapphire, spinel, olivine, garnet, and zircon. The supergene oxidation zones of some copper and iron deposits in Hubei and Anhui province host gem-quality turquoise and malachite. Moreover, the formation of the nephrite deposit in China is mostly related to the carbonatite and serpentinite rocks involved in the metamorphic-metasomatic processes. This paper comprehensively introduces the distribution of gemstones deposits, as well as the gemological and mineralogical characteristics of gemstones in China. Our present investigation provides insights into the gemstone potential of China for further exploitation.

Neuropathologic Findings in a Patient With Juvenile-Onset Levodopa Responsive Parkinsonism Due to ATP13A2 Mutation

Objective:To describe the postmortem neuropathological findings of a patient with Kufor Rakeb Syndrome (KRS) due to ATP13A2 mutation. KRS is characterized by juvenile-onset, levodopa-responsive parkinsonism associated with pyramidal signs, supranuclear gaze palsy, and cognitive impairment.Methods:Detailed neuropathological analysis of the brain. The patient had a genetically confirmed ATP13A2 homozygous missense mutation and died at age 38, 26 years after the onset of his symptoms.Results:The main brain neuropathological findings were widespread neuronal and glial lipofuscin accumulation with no Lewy-body type inclusions and absence of 𝛼-synuclein-, tau-, 𝛽-amyloid-, and TDP-43-protein-positive pathologies. Sparse iron deposits were observed in several brain areas but no obvious axonal spheroids were identified.Discussion:This is to our knowledge the first KRS postmortem neuropathological description. Iron deposits were found but were not associated with increased axonal spheroids, as frequently observed in neurodegeneration with brain iron accumulation (NBIA). ATP13A2 mutations have been described in patients with neuronal ceroid lipofuscinosis (CLN). Moreover animal models with these mutations develop neurodegenerative disorders with CLN pathology. Therefore, our findings support that ATP13A2 mutations may be considered a genetic etiology of neuronal lipofuscinosis.

DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84–0.88 and 0.40–0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75–0.81 and 0.62–0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.

Influence of the Structure of Iron Coatings on their Physical and Mechanical Properties

The influence of electrolysis parameters on the structure and properties of iron coatings is shown. The dependencies of hardness of iron coatings upon the parameters of metal microstructure characterized by dilatation are established. It is shown that dilatation can be used as a checkup parameter for iron strength properties. The influence of some factors on the properties of iron deposits obtained under non-stationary deposition regimes is analyzed.