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Coal use exacerbates several major environmental problems including build-up of greenhouse gases and air quality deterioration. Although Kuzbass (Siberia) is one of the largest exploited coal basins worldwide, the role of regional coal mining and processing in atmospheric pollution is unknown. We outlined the Kuzbass coal basin by spaceborne night-lights and revealed a regional, long-term tropospheric NO 2 anomaly (2005–2018) by spaceborne NO 2 column observations (hereafter ‒ NO 2 ). The spatial agreement between NO 2 and night-lights indicates that the anomaly is attributable to an agglomeration of coal quarries and the cities in Kuzbass, that are heavily reliant on coal. A positive relationship between NO 2 and interannual coal production suggested that the anomaly was related to coal in Kuzbass; ~ 1.0% of annual coal production increase induced ~ 0.5–0.6% of NO 2 enhancement. As coal production accelerated since 2010, NO 2 exhibited strikingly similar annual increases over Kuzbass in 2010–2014 (7%) and 2015–2019 (15%), compared to 2005–2009. Conversely, Siberian cities lacking a coal industry followed the global trend of reducing NO 2 for the same periods (−5% and −14%, respectively), driven by fuel combustion improvements. Overall, we demonstrated that coal mining, processing and utilization can induce distinct tropospheric NO 2 anomalies, detectable from space.

We studied and reconstructed a severe Central Asian dust storm of November 4, 2021, through high-resolution TROPOMI UVAI spaceborne observations, ground-based aerosol measurements, and Lagrangian particle modeling. The dust storm was caused by the front part of a cold polar anticyclone front from the Ural-Volga regions, which struck the central and eastern parts of Uzbekistan under favorable atmospheric conditions. Two plumes spread out, causing a thick haze to blanket the region. The most severe dust storm effects hit the capital of Uzbekistan (Tashkent) and the Fergana Valley, where the thick atmospheric dust layer dropped the visibility to 200 m. PM 10 concentrations reached 18,000 µg/m 3 (260-fold exceedance of the local long-term average). The PM 2.5 concentrations remained above 300 µg/m 3 for nearly ten days, indicating an extremely long-lasting event. The dust storm was caused by an extremely strong summer heatwave of 2021 in Kazakhstan with unprecedentedly high temperatures reaching 46.5 °C. The long-lasting drought dried up the soil down to 50 cm depth, triggering the soil cover denudation due to drying out vegetation and losing its moisture. This event was the worst since 1871 and considering the increasing aridity of Central Asia, the onset of potentially recurring severe dust storms is alarming.

Effective management of rain-fed agriculture in drought-prone countries like Ethiopia requires a comprehensive understanding of rainfall variability and improved predictive capabilities. There is a significant research gap remains in understanding the combined interaction and dynamic influence on rainfall patterns in the Upper Blue Nile Basin (UBNb). Moreover, vertically integrated water content and surface temperature have not been adequately explored for their potential to improve rainfall prediction. This study addresses these gaps by investigating the role of the TEJ in shaping summer rainfall variability over the UBNb. We utilize the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset from 1981 to 2022, specific humidity and wind data from the ECMWF Fifth Generation reanalysis (ERA5) for the period 1979–2022, 200 hPa wind data and SSTs from the NCEP reanalysis (1981–2022) are used both individually and in combination with other climatic variables at Bahir Dar, Debre Markos, Nekemte, and Gore. Ground-based station data from 1990 to 2020 serve to validate two neural network models: NARX-SST and NARX-SST-200 hPa ECMWF. We employed Artificial Neural Network (ANN) models to forecast rainfall by integrating CHIRPS rainfall data, total column water vapor (TCWV), and surface temperature for spatial prediction, while using sea surface temperature (SST), 200 hPa zonal wind, and ECMWF precipitation data for temporal forecasting. The ANN outputs demonstrate a strong association (correlation = 0.99) with the CHIRPS dataset, and low error values are identified. The spatio-temporal analysis significantly enhances the quality and precision of summer rainfall forecasts. This study contributes to improved rainfall prediction critical for sustainable water resource management and agricultural planning in Ethiopia and the broader Nile Basin. The study improves rainfall prediction using ANN models but is limited by the number of stations and variables, suggesting future work should include more data and broader climate drivers for greater accuracy.

Osteosarcoma (OS) is the most frequent pediatric malignant bone tumor that has a high propensity for metastases. Through osteoblast-specific alteration of p53 status, we developed a genetically engineered mouse model of localized and metastatic OS to gain an understanding into the molecular pathogenesis of OS. Microarray analysis of both localized tumors and metastatic tumors identified the downregulation of the naked cuticle homolog 2 (NKD2) gene, a negative regulator of Wnt signaling. Overexpression of NKD2 in metastatic human and mouse OS cells significantly decreases cell proliferation, migration and invasion ability in vitro and drastically diminishes OS tumor growth and metastasis in vivo , whereas downregulation enhances migratory and invasive potential. Evaluation of NKD2-overexpressing tumors revealed upregulation of tumor-suppressor genes and downregulation of molecules involved in blood vessel formation and cell migration. Furthermore, assessment of primary human OS revealed downregulation of NKD2 in metastatic and recurrent OS. Finally, we provide biological evidence that use of small-molecule inhibitors targeting the Wnt pathway can have therapeutic efficacy in decreasing metastatic properties in OS. Our studies provide compelling evidence that downregulation of NKD2 expression and alterations in associated regulated pathways have a significant role in driving OS tumor growth and metastasis.

Animals and plants interact with microbes by engaging specific surveillance systems, regulatory networks, and response modules that allow for accommodation of mutualists and defense against antagonists. Antimicrobial defense responses are mediated in both animals and plants by innate immunity systems that owe their functional similarities to convergent evolution. Like animals and plants, fungi interact with bacteria. However, the principles governing these relations are only now being discovered. In a study system of host and nonhost fungi interacting with a bacterium isolated from the host, we found that bacteria used a common gene repertoire to engage both partners. In contrast, fungal responses to bacteria differed dramatically between the host and nonhost. These findings suggest that as in animals and plants, the genetic makeup of the fungus determines whether bacterial partners are perceived as mutualists or antagonists and what specific regulatory networks and response modules are initiated during each encounter. Fungal-bacterial symbioses range from antagonisms to mutualisms and remain one of the least understood interdomain interactions despite their ubiquity as well as ecological and medical importance. To build a predictive conceptual framework for understanding interactions between fungi and bacteria in different types of symbioses, we surveyed fungal and bacterial transcriptional responses in the mutualism between Rhizopus microsporus ( Rm ) (ATCC 52813, host) and its Mycetohabitans (formerly Burkholderia ) endobacteria versus the antagonism between a nonhost Rm (ATCC 11559) and Mycetohabitans isolated from the host, at two time points, before and after partner physical contact. We found that bacteria and fungi sensed each other before contact and altered gene expression patterns accordingly. Mycetohabitans did not discriminate between the host and nonhost and engaged a common set of genes encoding known as well as novel symbiosis factors. In contrast, responses of the host versus nonhost to endobacteria were dramatically different, converging on the altered expression of genes involved in cell wall biosynthesis and reactive oxygen species (ROS) metabolism. On the basis of the observed patterns, we formulated a set of hypotheses describing fungal-bacterial interactions and tested some of them. By conducting ROS measurements, we confirmed that nonhost fungi increased production of ROS in response to endobacteria, whereas host fungi quenched their ROS output, suggesting that ROS metabolism contributes to the nonhost resistance to bacterial infection and the host ability to form a mutualism. Overall, our study offers a testable framework of predictions describing interactions of early divergent Mucoromycotina fungi with bacteria. IMPORTANCE Animals and plants interact with microbes by engaging specific surveillance systems, regulatory networks, and response modules that allow for accommodation of mutualists and defense against antagonists. Antimicrobial defense responses are mediated in both animals and plants by innate immunity systems that owe their functional similarities to convergent evolution. Like animals and plants, fungi interact with bacteria. However, the principles governing these relations are only now being discovered. In a study system of host and nonhost fungi interacting with a bacterium isolated from the host, we found that bacteria used a common gene repertoire to engage both partners. In contrast, fungal responses to bacteria differed dramatically between the host and nonhost. These findings suggest that as in animals and plants, the genetic makeup of the fungus determines whether bacterial partners are perceived as mutualists or antagonists and what specific regulatory networks and response modules are initiated during each encounter.

Understanding the temporal variability of atmospheric methane (CH4) and its potential drivers can advance the progress toward mitigating changes to the climate. To comprehend interannual variability and spatial characteristics of anomalous CH4 mole fractions and its drivers, we used integrated data from different platforms such as in situ measurements and satellites (TROPOspheric Monitoring Instrument (TROPOMI) and Greenhouse Gases Observing SATellite (GOSAT)) retrievals. A pronounced change of annual growth rate was detected at Anmyeondo (AMY), Republic of Korea, ranging from −16.8 to 31.3 ppb yr−1 as captured in situ through 2015–2020 and 3.9 to 16.4 ppb yr−1 detected by GOSAT through 2014–2019, respectively. High growth rates were discerned in 2016 (31.3 ppb yr−1 and 13.4 ppb yr−1 from in situ and GOSAT, respectively) and 2019 (27.4 ppb yr−1 and 16.4 ppb yr−1 from in situ and GOSAT, respectively). The high growth in 2016 was essentially explained by the strong El Niño event in 2015–2016, whereas the large growth rate in 2019 was not related to ENSO. We suggest that the growth rate that appeared in 2019 was related to soil temperature according to the Noah Land Surface Model. The stable isotopic composition of 13C/12C in CH4 (δ13-CH4) collected by flask-air sampling at AMY during 2014–2019 supported the soil methane hypothesis. The intercept of the Keeling plot for summer and autumn were found to be −53.3‰ and −52.9‰, respectively, which suggested isotopic signature of biogenic emissions. The isotopic values in 2019 exhibited the strongest depletion compared to other periods, which suggests even a stronger biogenic signal. Such changes in the biogenic signal were affected by the variations of soil temperature and soil moisture. We looked more closely at the variability of XCH4 and the relationship with soil properties. The result indicated a spatial distribution of interannual variability, as well as the captured elevated anomaly over the southwest of the domain in autumn 2019, up to 70 ppb, which was largely explained by the combined effect of soil temperature and soil moisture changes, indicating a pixel-wise correlation of XCH4 anomaly with those parameters in the range of 0.5–0.8 with a statistical significance (p < 0.05). This implies that the soil-associated drivers are able to exert a large-scale influence on the regional distribution of CH4 in Korea.

A cavity ring-down spectroscopy (CRDS) G-2401m analyzer onboard a Beechcraft King Air 350, a new Korean Meteorological Administration (KMA) research aircraft measurement platform since 2018, has been used to measure in situ CO2, CH4, and CO. We analyzed the aircraft measurements obtained in two campaigns: a within-boundary layer survey over the western Republic of Korea (hereafter Korea) for analyzing the CO2 and CH4 emission characteristics for each season (the climate change monitoring (CM) CM mission), and a low altitude survey over the Yellow Sea for monitoring the pollutant plumes transported into Korea from China (the environment monitoring (EM) mission). This study analyzed CO2, CH4, and CO data from a total of 14 flights during 2019 season. To characterize the regional combustion sources signatures of CO2 and CH4, we calculated the short-term (1-min slope based on one second data) regression slope of CO to CO2 and CH4 to CO enhancements (subtracted with background level, present as ∆CO, ∆CO2, and ∆CH4); slope filtered with correlation coefficients (R2) (<0.4 were ignored). These short-term slope analyses seem to be sensitive to aircraft measurements in which the instrument samples short-time varying mixtures of different air masses. The EM missions all of which were affected by pollutants emitted in China, show the regression slope between ∆CO and ∆CO2 with of 1.8–6% and 0.3–0.7 between ∆CH4 and ∆CO. In particular, the regression slope between ∆CO and ∆CO2 increased to >4% when air flows from east-central China such as Hebei, Shandong, and Jiangsu provinces, etc., sustained for 1–3 days, suggesting pollutants from these regions were most likely characterized by incomplete fossil fuel combustions at the industries. Over 80% of the observations in the Western Korea missions were attributed to Korean emission sources with regression slope between ∆CO and ∆CO2 of 0.5–1.9%. The CO2 emissions hotspots were mainly located in the north-Western Korea of high population density and industrial activities. The higher CH4 were observed during summer season with the increasing concentration of approximately 6% over the background level, it seems to be attributed to biogenic sources such as rice paddies, landfill, livestock, and so on. It is also noted that occurrences of high pollution episodes in North-Western Korea are more closely related to the emissions in China than in Korea.