Search

The specific source of ancient sedimentary manganese (Mn) deposits is commonly complex. Here we use systematic major and trace element data with strontium (Sr) and neodymium (Nd) isotopic analyses of the Ortokarnash Mn(II) carbonate ores and associated carbonate rocks from the Upper Carboniferous Kalaatehe Formation (ca. 320 Ma) in order to constrain the Mn source. This formation consists of three members: the first member is a volcanic breccia limestone, the second member is a sandy limestone, and the third member is a black marlstone with the Mn(II) carbonate interlayers. Petrographic observations in combination with low Al2O3 (2O3 and 87Sr/86Sr ratios as well as εNd(t) values demonstrate a negligible influence of terrigenous detrital contamination on both Sr and Nd isotopic compositions of the Mn(II) carbonate ores. The Sr isotopes of Mn(II) carbonate ores are most likely affected by post-depositional alteration, while Nd isotopes remain unaltered. The initial 87Sr/86Sr ratios in the associated carbonate rocks are likely the result of a mixture of the chemical components (i.e., seawater) and the Al-rich components (e.g., volcanoclastic material), while the detrital effects on Nd isotopes are negligible. In addition, both Sr and Nd isotopes in these non-mineralized wall rocks remained unchanged during post-depositional processes. The relatively low Th/Sc ratios and positive εNd(t) values suggest that the aluminosilicate fraction in the calcarenite and sandy limestone was mainly derived from the weathering of a depleted mafic source, representing the riverine input into the seawater. Given that the Mn(II) carbonate ores are characterized by negative εNd(t) values, these suggest that seafloor-vented hydrothermal fluids derived from interaction with the underlying old continental crust mainly contribute to the source of the Mn(II) carbonates.

Interleukin 10 (IL-10) is a synthetic inhibitor of human cytokines with immunomodulatory and anti-inflammatory effects. This study was designed to investigate the expression variation of IL-10 in the multiple sites including cortex, hippocampus, and lung tissues of neonatal hypoxic-ischemic (HI) rats and explore the crucial role of IL-10 in alleviating HI brain damage. In this study, neonatal Sprague-Dawley rats were subjected to the right common carotid artery ligation, followed by 2 h of hypoxia. The expression of IL-10 in the cortex, hippocampus, and lung tissues was measured with immunohistochemistry, real-time quantitative polymerase chain reaction (RT-qPCR), and western blot (WB). Immunofluorescence double staining was performed to observe the localization of IL-10 in neurons and astrocytes. Moreover, not-targeting and targeting IL-10 siRNA lentivirus vectors were injected into the rats of the negative control (NC) and IL-10 group, respectively, and the mRNA levels of B-cell lymphoma 2 (Bcl-2) and endoplasmic reticulum protein 29 (ERp29) were detected by RT-qPCR following IL-10 silence. The results demonstrated that the IL-10 expression was markedly increased after HI and IL-10 were colocalized with neurons and astrocytes which were badly injured by HI insult. In addition, Bcl-2 and ERp29 were remarkably decreased following IL-10 mRNA interference compared with the NC group. Our findings revealed that IL-10 exerted its antiapoptotic and neuroprotective effects by regulating the expression of Bcl-2 and ERp29, indicating that IL-10 may be a promising molecule target for HIE treatment.

Brain age prediction models using diffusion magnetic resonance imaging (dMRI) and machine learning techniques enable individual assessment of brain aging status in healthy people and patients with brain disorders. However, dMRI data are notorious for high intersite variability, prohibiting direct application of a model to the datasets obtained from other sites. In this study, we generalized the dMRI-based brain age model to different dMRI datasets acquired under different imaging conditions. Specifically, we adopted a transfer learning approach to achieve domain adaptation. To evaluate the performance of transferred models, brain age prediction models were constructed using a large dMRI dataset as the source domain, and the models were transferred to three target domains with distinct acquisition scenarios. The experiments were performed to investigate (1) the tuning data size needed to achieve satisfactory performance for brain age prediction, (2) the feature types suitable for different dMRI acquisition scenarios, and (3) performance of the transfer learning approach compared with the statistical covariate approach. By tuning the models with relatively small data size and certain feature types, optimal transferred models were obtained with significantly improved prediction performance in all three target cohorts (p ​

Abstract Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. Methods Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. Results Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. Conclusion By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals.

Acupuncture has been used for 1000 years, but how it gives therapeutic effect is not completely understood. Integration of brain wave as the central nervous system plays an important role in acupuncture efficacy. To explore the effect of acupuncture stimulation in the central nervous system, it is imperative to decide the adjustments in brain waves that produced because of acupuncture stimulation. Electroencephalogram (EEG) is often used in the acupuncture research area. EEG records spontaneous cerebral activity after some time by positioning electrodes on scalp surface. Using EEG, we can observe the brain connectivity regarding the acupuncture stimulation. An overview regarding the effect of manual acupuncture on human brain wave through EEG observation will be discussed in this review.