Data-driven machine learning technology can learn and extract features, a factor which is well recognized to be powerful in the warning and prediction of severe weather. With the large-scale ...deployment of the radar wind profile (RWP) observational network in China, dynamical variables with higher temporal and spatial resolution in the vertical become strong supports for machine-learning-based severe convection prediction. Based on the RWP mesonet that has been deployed in Beijing, this study uses the measurements from four triangles composed of six RWP stations to determine the profiles of divergence, vorticity, and vertical velocity before rainfall onsets. These dynamic feature variables, combined with cloud properties from Himawari-8 and ERA-5 reanalysis, serve as key input parameters for two rainfall forecast models based on the random forest (RF) classification algorithm. One is for the rainfall/non-rainfall forecast and another for the rainfall grade forecast. The roles of dynamic features such as divergence, vorticity, and vertical velocity are examined from ERA-5 reanalysis data and RWP measurements. The contribution of each feature variable to the performance of the RF model in independent tests is also discussed here. The results show that the usage of RWP observational data as the RF model input tends to result in better performance in rainfall/non-rainfall forecast 30 min in advance of rainfall onset than using the ERA-5 data as inputs. For the rainfall grade forecast, the divergence and vorticity that were estimated from the RWP measurements at 800 hPa show importance in improving the model performance in heavy and moderate rain forecasts. This indicates that the atmospheric dynamic variable measurements from RWP have great potential to improve the prediction skill of convection with the aid of a machine learning model.
Aeolus is the first satellite mission to directly observe
wind profile information on a global scale. After implementing a set of bias
corrections, the Aeolus data products went public on ...12 May 2020. However,
Aeolus wind products over China have thus far not been evaluated extensively by
ground-based remote sensing measurements. In this study, the Mie-cloudy and
Rayleigh-clear wind products from Aeolus measurements are validated against
wind observations from the radar wind profiler (RWP) network in China. Based
on the position of each RWP site relative to the closest Aeolus ground
tracks, three matchup categories are proposed, and comparisons between Aeolus
wind products and RWP wind observations are performed for each category
separately. The performance of Mie-cloudy wind products does not change much
between the three matchup categories. On the other hand, for Rayleigh-clear
and RWP wind products, categories 1 and 2 are found to have much smaller
differences compared with category 3. This could be due to the RWP site
being sufficiently approximate to the Aeolus ground track for categories 1 and
2. In the vertical, the Aeolus wind products are similar to the RWP wind
observations, except for the Rayleigh-clear winds in the height range of
0–1 km. The mean absolute normalized differences between the
Mie-cloudy (Rayleigh-clear) and the RWP wind components are 3.06 (5.45),
2.79 (4.81), and 3.32 (5.72) m/s at all orbit times and ascending and
descending Aeolus orbit times, respectively. This indicates that the wind
products for ascending orbits are slightly superior to those for descending
orbits, and the observation time has a minor effect on the comparison. From
the perspective of spatial differences, the Aeolus Mie-cloudy winds are
consistent with RWP winds in most of east China, except in coastal areas
where the Aeolus Rayleigh-clear winds are more reliable. Overall, the
correlation coefficient R between the Mie-cloudy (Rayleigh-clear) wind and RWP
wind component observation is 0.94 (0.81), suggesting that Aeolus wind
products are in good agreement with wind observations from the RWP network
in China. The findings give us sufficient confidence in assimilating the
newly released Aeolus wind products in operational weather forecasting in
China.
Aerosol-cloud (AC) interactions remain uncharacterized due to difficulties in obtaining accurate aerosol and cloud observations. In this study, we quantified the aerosol indirect effects (AIE) on ...warm clouds over Eastern China based on near-simultaneous retrievals from MODIS/AQUA, CALIOP/CALIPSO, and CPR/CLOUDSAT between June 2006 and December 2010. The seasonality of aerosols from ground-based PM10 (aerosol particles with diameter of 10 μm or less) significantly differed from that estimated using MODIS aerosol optical depth (AOD). This result was supported by the lower level frequency profile of aerosol occurrence from CALIOP, indicative of the significant role of CALIOP in the AC interaction. To focus on warm clouds, cloud layers with base (top) altitudes above 7 (10) km were excluded. The combination of CALIOP and CPR was applied to determine the exact position of warm clouds relative to aerosols out of the following six scenarios in terms of AC mixing states: 1) aerosol only (AO); 2) cloud only (CO); 3) single aerosol layer-single cloud layer (SASC); 4) single aerosol layer-double cloud layers (SADC); 5) double aerosol layers - single cloud layer (DASC); and 6) others. The cases with vertical distance between aerosol and cloud layer less (more) than 100 m (700 m) were marked mixed (separated), and the rest as uncertain. Results showed that only 8.95% (7.53%) belonged to the mixed (separated and uncertain) state among all of the collocated AC overlapping cases, including SASC, SADC, and DASC. Under mixed conditions, the cloud droplet effective radius (CDR) decreased with increasing AOD at moderate aerosol loading (AOD<0.4), and then became saturated at an AOD of around 0.5, followed by an increase in CDR with increasing AOD, known as boomerang shape. Under separated conditions, no apparent changes in CDR with AOD were observed. We categorized the AC dataset into summer- and winter-season subsets to determine how the boomerang shape varied with season. The response of CDR to AOD in summer exhibited similar but much more deepened boomerang shape, as compared with the all year round case. In contrast, CDR in winter did not follow the boomerang shape for its continued decreasing with increasing AOD, even after the saturation zone (AOD around 0.5) of a cloud droplet.
•Aerosol-warm cloud interaction in eastern China is examined using multi-sensors.•The aerosol-cloud mixing state is identified by vertical aerosol and cloud layers.•Aerosol-cloud interaction strength is investigated during summer- and winter-season.•Cloud droplet radii response to aerosol follows a boomerang shape for mixed cases.•No apparent change in cloud droplet radii with AOD can be found for separated cases.
Objectives
Ritonavir and cobicistat are strong inhibitors of human cytochrome P450‐3A (CYP3A) isoforms, and are used clinically as pharmacokinetic boosting agents for other antiretroviral drugs. Data ...reported by the manufacturer suggest that cobicistat is a more selective inhibitor of CYP3A than ritonavir. However, this claim has not been validated in clinical studies. This study evaluated the in‐vitro inhibitory potency of ritonavir and cobicistat vs a series of human CYP isoforms.
Method
The model system utilized human liver microsomes and isoform‐selective index substrates.
Key findings
Ritonavir and cobicistat both were strong inhibitors of CYP3A4, with IC50 values of 0.014 and 0.032 μm, respectively. A component of inhibition was time‐dependent (mechanism‐based). Neither drug meaningfully inhibited CYP1A2 (IC50 > 150 μm). CYP2B6, CYP2C9, CYP2C19 and CYP2D6 were inhibited by both drugs, but with IC50 values exceeding 6 μm.
Conclusions
Consistent with previous reports, both ritonavir and cobicistat were highly potent inhibitors of CYP3A. Both drugs were weaker inhibitors of other human CYPs, with IC50 values at least two orders of magnitude higher. There was no evidence of a meaningful difference in selectivity between the two drugs.
Consensus has been reached that precipitation extremes vary proportionally with global warming. Nevertheless, the underlying cause and magnitude of these factors affecting their relationships remain ...highly debated. To elucidate the complex relationship between precipitation extremes and temperature in China during the warm seasons (May through September), a 60-year (1958–2017) record of hourly rain gauge measurements, in combination with surface air temperature, RH, precipitable water (PW), and convective available potential energy (CAPE) collected from 120 radiosonde stations were examined. Spatially, the scaling relationship between precipitation extremes and temperature exhibits a large geographic difference across China. In particular, the Clausius–Clapeyron (CC) and sub-CC relationships tend to occur in northwest (ROI-N) and southeast China (ROI-S), whereas the super-CC relationship is found to mainly concentrates in central China (ROI-C). Additionally, the response of precipitation extremes to temperature becomes more sensitive as precipitation intensity increases, shifting from CC to super-CC at a certain point of inflection that varies by geographic regions. This shift occurs at approximately 15 °C in ROI-C and ROI-N, but at around 20 °C in ROI-S. Within the temperature range of the super-CC slope, the PW rises with the increases in temperature, whereas the CAPE decreases with rising temperature, which is contrary to the monotonic scaling of precipitation with temperature. From the perspective of interannual variation, the precipitation extremes correlate positively with temperature. This further confirms the notion that global warming, through jointly affecting PW and CAPE, is able to considerably regulate precipitation extremes.
The satellite-based quantification of cloud radiative forcing remains poorly understood, due largely to the limitation or uncertainties in characterizing cloud-base height (CBH). Here, we use the CBH ...data from radiosonde measurements over China in combination with the collocated cloud-top height (CTH) and cloud properties from MODIS/Aqua to quantify the impact of CBH on shortwave cloud radiative forcing (SWCRF). The climatological mean SWCRF at the surface (SWCRF
SUR
), at the top of the atmosphere (SWCRF
TOA
), and in the atmosphere (SWCRF
ATM
) are estimated to be −97.14, −84.35, and 12.79 W m
−2
, respectively for the summers spanning 2010 to 2018 over China. To illustrate the role of the cloud base, we assume four scenarios according to vertical profile patterns of cloud optical depth (COD). Using the CTH and cloud properties from MODIS alone results in large uncertainties for the estimation of SWCRF
ATM
, compared with those under scenarios that consider the CBH. Furthermore, the biases of the CERES estimation of SWCRF
ATM
tend to increase in the presence of thick clouds with low CBH. Additionally, the discrepancy of SWCRF
ATM
relative to that calculated without consideration of CBH varies according to the vertical profile of COD. When a uniform COD vertical profile is assumed, the largest SWCRF discrepancies occur during the early morning or late afternoon. By comparison, the two-point COD vertical distribution assumption has the largest uncertainties occurring at noon when the solar irradiation peaks. These findings justify the urgent need to consider the cloud vertical structures when calculating the SWCRF which is otherwise neglected.
Urbanization and air pollution are major anthropogenic impacts on Earth’s environment, weather, and climate. Each has been studied extensively, but their interactions have not. Urbanization leads to ...a dramatic variation in the spatial distribution of air pollution (fine particles) by altering surface properties and boundary-layer micrometeorology, but it remains unclear, especially between the centers and suburbs of metropolitan regions. Here, we investigated the spatial variation, or inhomogeneity, of air quality in urban and rural areas of 35 major metropolitan regions across China using four different long-term observational datasets from both ground-based and space-borne observations during the period 2001–2015. In general, air pollution in summer in urban areas is more serious than in rural areas. However, it is more homogeneously polluted, and also more severely polluted in winter than that in summer. Four factors are found to play roles in the spatial inhomogeneity of air pollution between urban and rural areas and their seasonal differences: (1) the urban–rural difference in emissions in summer is slightly larger than in winter; (2) urban structures have a more obvious association with the spatial distribution of aerosols in summer; (3) the wind speed, topography, and different reductions in the planetary boundary layer height from clean to polluted conditions have different effects on the density of pollutants in different seasons; and (4) relative humidity can play an important role in affecting the spatial inhomogeneity of air pollution despite the large uncertainties.
Abstract Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the ...inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data—a task at which the benchmarked single-omics methods proved inadequate.
Abstract
Large-scale
in situ
observations are sorely lacking, leading to poor understanding of nationwide atmospheric turbulence over China. Nevertheless, high-resolution soundings have become ...available starting in 2011, providing a unique opportunity to investigate turbulence across China. Here, we calculated the mean turbulence dissipation rate (
ϵ
) from radiosonde measurements across China for the period 2011–2018 using Thorpe analysis. The atmospheric layers that had stronger turbulence indicated by larger
ϵ
generally came with larger Thorpe length but with smaller Brunt–Väisälä frequency. Overall, the clear-air
ϵ
in the free atmosphere exhibited large spatial variability with a ‘south-high north-low’ pattern. Large clear-air
ϵ
values were observed in both the lower stratosphere (LS) and upper troposphere (UT), especially over the Tibetan Plateau (TP) and its neighboring regions with complex terrain likely due to large-amplitude mountain waves. Particularly, less frequent but more intense clear-air turbulence was observed in both lower troposphere (LT) and UT over the TP, while more frequent, less intense clear-air turbulence was found in northern China. The all-sky turbulence considering the moist-saturation effects was much stronger in the troposphere, notably in southern China where convective clouds and precipitation oftentimes dominated. In the vertical direction, the altitude of peak clear-air
ϵ
in the troposphere was found to decrease poleward, broadly consistent with the meridional gradient of tropopause height in the Northern Hemisphere. A double-peak mode stood out for the profiles of clear-air
ϵ
at midlatitudes to the north of 30° N in winter: one peak was at altitudes of 15–18 km, and another at altitudes of 5–8 km. The strong shear instabilities around the westerly jet stream could account for the vertical bimodal structures. The seasonality of
ϵ
was also pronounced, reaching maxima in summer and minima in winter. Our results may help understand and avoid clear-air turbulence, as related to aviation safety among other issues.
Basal-like breast cancer (BLBC) is an aggressive breast cancer subtype with features similar to the basal cells surrounding the mammary ducts. Treatment of patients with BLBC has been challenging due ...to the lack of well-defined molecular targets. Due to the clinical and pathological similarities of BLBC with BRCA-deficient breast cancers, the effectiveness of Poly (ADP-ribose) polymerase inhibitors (PARPi) has been tested in early phase clinical trials for patients with advanced BLBC, with limited clinical responses. Recently, it was reported that HORMAD1 overexpression sensitizes BLBC to HR-targeting agents by suppressing homologous recombination. Our independent analysis suggests that HORMAD1 is aberrantly overexpressed in about 80% of BLBC, and its expression in normal tissues is restricted to testis. Our experimental data suggests that HORMAD1 overexpression correlates with focal hypomethylation in BLBC. On the other hand, investigation of the Genomics of Drug Sensitivity in Cancer dataset revealed significantly reduced sensitivity of HORMAD1-overexpressing BLBC cell lines to Rucaparib, a commonly used PARPi. To further assess the role of HORMAD1 in PARPi sensitivity, we generated three HORMAD1-overexpressing xenograft models using the HORMAD1-low BLBC cell lines HCC1954, HCC1806, and BT20; we then subjected these xenograft models to Rucaparib treatment. Ectopic expression of HORMAD1 enhances tumor formations in two of these models, and significantly reduces sensitivity to Rucaparib in the HCC1954 model. Taken together, our data suggest that epigenetic activation of HORMAD1 by hypomethylation in BLBC may endow reduced sensitivity to Rucaparib treatment in some tumor models.
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