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The cloud amount, referred to as the frequency of cloud occurrences, is of great importance for the Earth–atmosphere system. It was conventionally quantified as the area fraction of clouds in a given region, discarding the three-dimensional nature of both cloud entities and their spatial distribution. Although the area fraction is explicit, it is the volume fraction that fully depicts cloud occurrences, and the area fraction is just related to a projection of the volume fraction. In this study, by using spaceborne radar measurements, the spatial distribution of cloud volume fraction throughout the troposphere was investigated, and the contributions of various cloud types at each location were clarified. Overall, the volume fraction of total clouds in the whole troposphere is 15.9%, while the corresponding area fraction relative to the global surface is 73.6%. The peak volume fraction occurs at 1 km altitude, mainly contributed by stratocumulus and cumulus. For a single cloud type, the maximum fraction is 48.8%, which is from stratocumulus and occurs at 1 km altitude above the Greenland Sea. Half of the eight cloud types, altostratus, cirrus, nimbostratus, and deep convective clouds, reach the nominal tropopause. In particular, the vertical distribution difference among multiple cloud types in each category (low-level, middle-level, and vertically extending) was clarified, and it was found that the dominant cloud type in a category varies notably with the location in the atmosphere.

Satellite rainrate estimation is a great challenge, especially in mesoscale convective systems (MCSs), which is mainly due to the absence of a direct physical connection between observable cloud parameters and surface rainrate. The machine learning technique was employed in this study to estimate rainrate in the MCS domain via using cloud top temperature (CTT) derived from a geostationary satellite. Five kinds of machine learning models were investigated, i.e., polynomial regression, support vector machine, decision tree, random forest, and multilayer perceptron, and the precipitation of Climate Prediction Center morphing technique (CMORPH) was used as the reference. A total of 31 CTT related features were designed to be the potential inputs for training an algorithm, and they were all proved to have a positive contribution in modulating the algorithm. Random forest (RF) shows the best performance among the five kinds of models. By combining the classification and regression schemes of the RF model, an RF-based hybrid algorithm was proposed first to discriminate the rainy pixel and then estimate its rainrate. For the MCS samples considered in this study, such an algorithm generates the best estimation, and its accuracy is definitely higher than the operational precipitation product of FY-4A. These results demonstrate the promising feasibility of applying a machine learning technique to solve the satellite precipitation retrieval problem.

Low-level warm clouds are a major component in multilayered cloud systems and they are generally hidden from the top-down view of satellites with passive measurements. This study conducts an investigation on oceanic warm clouds embedded in multilayered structures by using spaceborne radar data with fine vertical resolution. The occurrences of warm cloud overlapping and the geometric features of several kinds of warm cloud layers are examined. It is found that there are three main types of cloud systems that involve warm cloud layers, including warm single layer clouds, cold-warm double layer clouds, and warm-warm double layer clouds. The two types of double layer clouds account for 23% and in the double layer occurrences warm-warm double layer subsets contribute about 13%. The global distribution patterns of these three types differ from each other. Single-layer warm clouds and the lower warm clouds in the cold-warm double layer system they have nearly identical geometric parameters, while the upper and lower layer warm clouds in the warm-warm double layer system are distinct from the previous two forms of warm cloud layers. In contrast to the independence of the two cloud layers in cold-warm double layer system, the two kinds of warm cloud layers in the warm-warm double layer system may be coupled. The distance between the two layers in the warm-warm double layer system is weakly dependent on cloud thickness. Given the upper and lower cloud layer with moderate thickness of around 1 km, the cloudless gap reaches its maximum when exceeding 600 m. The cloudless gap decreases in thickness as the two cloud layers become even thinner or thicker.

Here, we describe mutants of Mycoplasma pulmonis that were obtained using a minitransposon, Tn4001TF1, which actively transposes but is then unable to undergo subsequent excision events. Using Tn4001TF1, we disrupted 39 genes previously thought to be essential for growth. Thus, the number of genes required for growth has been overestimated. This study also revealed evidence of gene duplications in M. pulmonis and identified chromosome segregation proteins that are dispensable in mycoplasmas but essential in Bacillus subtilis.

Precipitation nowcasting, as a crucial component of weather forecasting, focuses on predicting very short-range precipitation, typically within six hours. This approach relies heavily on real-time observations rather than numerical weather models. The core concept involves the spatio-temporal extrapolation of current precipitation fields derived from ground radar echoes and/or satellite images, which was generally actualized by employing computer image or vision techniques. Recently, with stirring breakthroughs in artificial intelligence (AI) techniques, deep learning (DL) methods have been used as the basis for developing novel approaches to precipitation nowcasting. Notable progress has been obtained in recent years, manifesting the strong potential of DL-based nowcasting models for their advantages in both prediction accuracy and computational cost. This paper provides an overview of these precipitation nowcasting approaches, from which two stages along the advancing in this field emerge. Classic models that were established on an elementary neural network dominated in the first stage, while large meteorological models that were based on complex network architectures prevailed in the second. In particular, the nowcasting accuracy of such data-driven models has been greatly increased by imposing suitable physical constraints. The integration of AI models and physical models seems to be a promising way to improve precipitation nowcasting techniques further. Key words: precipitation nowcasting; deep learning; neural network; classic model; large model

Many endophytic fungi are known to protect plants from plant pathogens, but the antagonistic mechanism has rarely been revealed. In this study, we wished to learn whether an endophytic Aspergillus sp., isolated from Taxus mairei, would indeed produce bioactive components, and if so whether (a) they would antagonize plant pathogenic fungi; and (b) whether this Aspergillus sp. would produce the compound also under conditions of confrontation with these fungi. The endophytic fungal strain from T. mairei was identified as Aspergillus clavatonanicus by analysis of morphological characteristics and the sequence of the internal transcribed spacers (ITS rDNA) of rDNA. When grown in surface culture, the fungus produced clavatol (2',4'-dihydroxy-3',5'-dimethylacetophenone) and patulin (2-hydroxy-3,7-dioxabicyclo [4.3.0]nona-5,9-dien-8-one), as shown by shown by NMR, MS, X-ray, and EI-MS analysis. Both exhibited inhibitory activity in vitro against several plant pathogenic fungi, i.e., Botrytis cinerea, Didymella bryoniae, Fusarium oxysporum f. sp. cucumerinum, Rhizoctonia solani, and Pythium ultimum. During confrontation with P. ultimum, A. clavatonanicus antagonized its growth of P. ultimum, and both clavatol as well as patulin were formed as the only bioactive components, albeit with different kinetics. We conclude that A. clavatonanicus produces clavatol and patulin, and that these two polyketides may be involved in the protection of T. mairei against attack by plant pathogens by this Aspergillus sp.

Introduction Most hepatocellular carcinomas (HCC) develop in a background of underlying liver disease including chronic hepatitis B. However, the effect of antiviral therapy on the long‐term outcome of patients with hepatitis B virus (HBV)‐related HCC treated with chemoembolization is unclear. This study aimed to evaluate the survival benefits of anti‐HBV therapy after chemoembolization for patients with HBV‐related HCC. Methods A total of 224 HCC patients who successfully underwent chemoembolization were identified, and their survival and other relevant clinical data were reviewed. Kaplan‐Meier and Cox regression analyses were performed to validate possible effects of antiviral treatment on overall survival (OS). Results The median survival time (MST) was 15.9 (95% confidence interval [CI], 9.5–27.7) months in the antiviral group and 9.6 (95% CI, 7.8–13.7) months in the non‐antiviral group (log‐rank test, P = 0.044). Cox multivariate analysis revealed that antiviral treatment was a prognostic factor for OS (P = 0.008). Additionally, a further analysis was based on the stratification of the TNM tumor stages. In the subgroup of early stages, MST was significantly longer in the antiviral‐treatment group than in the non‐antiviral group (61.8 months [95% CI, 34.8 months to beyond the follow‐up period] versus 26.2 [95% CI, 14.5–37.7] months, P = 0.012). Multivariate analysis identified antiviral treatment as a prognostic factor for OS in the early‐stage subgroup (P = 0.006). However, in the subgroup of advanced stages, MST of the antiviral‐treated group was comparable to that of the non‐antiviral group (8.4 [95% CI, 5.2–13.5] months versus 7.4 [95% CI, 5.9–9.3] months, P = 0.219). Multivariate analysis did not indicate that antiviral treatment was a significant prognostic factor in this subgroup. Conclusion Antiviral treatment is associated with prolonged OS time after chemoembolization for HCC, especially in patients with early‐stage tumors.

Background：The expression of TES,a novel tumor suppressor gene,is found to be down-regulated in the left anterior descending aorta of patients with coronary artery disease （CAD） compared with non-CAD subjects.This study aimed to investigate the expression of TES during the development of atherosclerosis in rabbits.Methods：Thirty-two New Zealand rabbits were randomly divided into a normal diet （ND） and high-fat diet （HFD） groups.Body weight and serum lipid levels were measured at 0,4,and 12 weeks after diet treatment.The degree of atherosclerosis in thoracic aortas was analyzed by histological examinations.The expression of Testin in the tissue samples was inspected via immunohistochemical and immunofluorescence confocal microscopy.Real time-polymerase chain reaction and Western blot analysis were performed to evaluate the expression of TES/Testin at mRNA and protein levels in the aortic tissues.Results：After 12 weeks postenrollment,rabbits in HFD group had a higher level of serum lipids and atherosclerotic plaque compared to ND group （P 〈 0.05）.Testin expression was detected at high levels in the endothelium and a weak expression on the subendothelium area.The expression of TES mRNA was markedly reduced by 10-fold in the aortic tissues in the HFD group compared with the ND group （P =0.015）,and the protein level was also significantly decreased in the HFD group （P 〈 0.05）.Conclusions：Reduced TES/Testin expression is associated with the development of atherosclerosis,implicating a potentially important role in the pathogenesis of atherosclerosis.

Background Severe air pollution in China threatens human health, and its negative impact decreases the urban settlement intentions of migrants in destination cities. We establish a comprehensive framework based on the push-pull migration model to investigate this phenomenon. Methods We employ a logistic model to analyze air pollution’s impact on the settlement intentions of the floating population based on the CMDS 2017 in China, combining the city-level socioeconomic variables with the individual-level variables. Results Our results show that the annual average concentration of PM2.5 increases by 1 unit and that the probability of migrants’ settlement intentions will decrease by 8.7%. Using a heterogeneity analysis, we find that the following migrant groups are more sensitive to air pollution: males, people over 30 years old, less educated people, and migrants with nonagricultural hukou . With every 1 unit increase in PM2.5, each group’s settlement intentions decrease by 13.2, 16.7, 16.9, and 12.6%, respectively. Conclusions Our results are consistent with existing studies. This study discovers that both external environment and internal factors influence migrants’ settlement intentions. Specifically, the differences in population sizes, economic development levels, public services, infrastructure conditions, and environmental regulations between cities play a significant role in migration decisions. We also confirm heterogeneous sensitivities to air pollution of different migrant subgroups in terms of individual characteristics, family factors, migration features, social and economic attributes.