Single-cell RNA sequencing reveals transdifferentiation of parathyroid chief cells into oxyphil cells in patients with uremic secondary hyperparathyroidism.

The parathyroid gland is one of the main organs that regulate calcium and phosphorus metabolism. It is mainly composed of chief cells and oxyphil cells. Oxyphil cell counts are low in the parathyroid glands of healthy adults but are dramatically increased in patients with uremia and secondary hyperparathyroidism (SHPT). Increased oxyphil cell counts are related to drug treatment resistance, but the origin of oxyphil cells and the mechanism of proliferation remain unknown. Herein, three types of parathyroid nodules (chief cell nodules, oxyphil cell nodules and mixed nodules, respectively) excised from parathyroid glands of uremic SHPT patients were used for single-cell RNA sequencing (scRNA-seq), other molecular biology studies, and transplantation into nude mice. Through scRNA-seq of parathyroid mixed nodules from three patients with uremic SHPT, we established the first transcriptomic map of the human parathyroid and found a chief-to-oxyphil cell transdifferentiation characterized by gradual mitochondrial enrichment associated with the uremic milieu. Notably, the mitochondrial enrichment and cellular proliferation of chief cell and oxyphil cell nodules decreased significantly after leaving the uremic milieu via transplantation into nude mice. Remarkably, the phenotype of oxyphil cell nodules improved significantly in the nude mice as characterized by decreased mitochondrial content and the proportion of oxyphil cells to chief cells. Thus, our study provides a comprehensive single-cell transcriptome atlas of the human parathyroid and elucidates the origin of parathyroid oxyphil cells and their underlying transdifferentiating mechanism. These findings enhance our understanding of parathyroid disease and may open new treatment perspectives for patients with chronic kidney disease.

An unusual high ozone event over the North and Northeast China during the record-breaking summer in 2018.

Under the background of global warming, the summer temperature of the North and Northeast China (NNEC) has significantly increased since 2017, which was accompanied by the aggravated ozone (O3) pollution. In 2018, the NNEC experienced a record-breaking summer of the past 40 years. Influenced by the abnormal high temperatures, a regional ozone event occurred on 2-3 August, over 63% of 79 selected cities in the NNEC were exposed to O3 pollution, and the maximum value of MDA8 O3 reached 268 µg/m3. Observations indicated that ozone concentrations agree well with the maximum temperature at 2 meters (MT2M) over NNEC with a correlation coefficient of 0.69. During the pollution episode, strong downdraft in the local high (35°N-42.5°N, 112.5°E-132.5°E; LH) over the NNEC created the favourable meteorological conditions for O3 formation. By analyzing the horizontal wind and wave activity fluxes (WAFs) at 200 hPa, we found that the LH formation was resulted from the Rossby wave propagation from upstream along the mid-latitude Asian jet. The split polar vortex intrusion further strengthened the amplitude of the Rossby wave and reinforced the LH. Moreover, a secondary circulation between Typhoon Jongdari and the LH contributed to the enhanced LH with strong subsidence. On the other hand, the stratospheric intrusions under the deep subsidence also contributed to the enhanced surface O3. In this study, the deep-seated meteorological dynamical mechanisms contributing to the abnormal high temperatures were investigated, which can lead to a better understanding of the regional O3 pollution over NNEC under the global-warming background.

Meteorological mechanism for a large-scale persistent severe ozone pollution event over eastern China in 2017.

An intensive and persistent regional ozone pollution event occurred over eastern China from 25 June to 5 July 2017. 73 out of 96 selected cities, most located in the Beijing-Tianjin-Hebei and the surrounding area (BTHS), suffered severe ozone pollution. A north-south contrast ozone distribution, with higher ozone (199 ± 33 µg/m3) in the BTHS and lower ozone (118 ± 25 µg/m3) in the Yangtze River Delta (YRD), was found to be dominated by the position of the West Pacific Subtropical High (WPSH) and mid-high latitude wave activities. In the BTHS, the positive anomalies of geopotential height at 500 hPa and temperature at the surface indicated favorable meteorological conditions for local ozone formation. Prevailing northwesterly winds in the mid-high troposphere and warm advection induced by weak southerly winds in the low troposphere resulted in low-moderate relative humidity (RH), less total cloud cover (TCC), strong solar radiation and high temperatures. Moreover, southerly winds prevailing over the BTHS aggravated the pollution due to regional transportation of O3 and its precursors. On one hand, the deep sinking motion and inversion layer suppressed the dispersion of pollutants. On the other hand, O3-rich air in the upper layer was maintained at night due to temperature inversion, which facilitated O3 vertical transport to the surface in the next-day morning due to elevated convection. Generally, temperature, UV radiation, and RH showed good correlations with O3 in the BTHS, with rates of 8.51 (µg/m3)/°C (within the temperature range of 20-38°C), 59.54 (µg/m3)/(MJ/m2) and -1.93 (µg/m3)/%, respectively.

Role of cyclone activity in summer precipitation over the northern margin of Eurasia.

In recent decades, the atmospheric moisture capacity has increased globally in concert with global warming, with a particularly notable warming trend in Arctic regions. However, due to limited observational data, the variation and causes of polar precipitation, especially large-scale precipitation events associated with Arctic cyclones, remain unclear. In this paper, GPM satellite data are compared with ERA5 reanalysis data to explore the characteristics of summer precipitation at the northern margin of the Eurasian region (NMER) and the influence of cyclone activity on precipitation. It is revealed that high precipitation values in the Arctic region, as indicated by the GPM and ERA5 data, are mainly concentrated at the NMER. However, the GPM data show an overall larger precipitation amount, while the station observations more closely agree with the ERA5 precipitation changes at the NMER. The cyclone identification results indicate that summer cyclones at the NMER are mainly distributed in the Barents, Kara and Laptev Seas, and the precipitation contribution rate of ERA5-derived cyclones is 37.35%, which is significantly higher than that of GPM-derived cyclones (29.47%). Furthermore, high cyclone activity results in more intense precipitation, with the top 5% of the strongest cyclones contributing 60% (GPM) and 40% (ERA5) to the total cyclonic precipitation.

A mask R-CNN model for reidentifying extratropical cyclones based on quasi-supervised thought.

The applications of machine learning/deep learning (ML/DL) methods in meteorology have developed considerably in recent years. Massive amounts of meteorological data are conducive to improving the training effect and model performance of ML/DL, but the establishment of training datasets is often time consuming, especially in the context of supervised learning. In this paper, to identify the two-dimensional (2D) structures of extratropical cyclones in the Northern Hemisphere, a quasi-supervised reidentification method for extratropical cyclones is proposed. This method first uses a traditional automatic cyclone identification method to construct a trainable labeled dataset and then reidentifies extratropical cyclones in a quasi-supervised fashion by using a (pre-trained) Mask region-based convolutional neural network (Mask R-CNN) model. In comparison, the new method increases the number of identified cyclones by 8.29%, effectively supplementing the traditional method. The newly recognized cyclones are mainly shallow or moderately deep subsynoptic-scale cyclones. However, a considerable portion of the new cyclones along the coastlines of the oceans are accompanied by strong winds. In addition, the Mask R-CNN model also shows good performance in identifying the horizontal structures of tropical cyclones. The quasi-supervised concept proposed in this paper may shed some light on accurate target identification in other research fields.