Performance of mNGS in bronchoalveolar lavage fluid for the diagnosis of invasive pulmonary aspergillosis in non-neutropenic patients

The diagnosis of invasive pulmonary aspergillosis (IPA) diseases in non-neutropenic patients remains challenging. It is essential to develop optimal non-invasive or minimally invasive detection methods for the rapid and reliable diagnosis of IPA. Metagenomic next-generation sequencing (mNGS) in bronchoalveolar lavage fluid (BALF) can be a valuable tool for identifying the microorganism. Our study aims to evaluate the performance of mNGS in BALF in suspected IPA patients and compare it with other detection tests, including serum/BALF galactomannan antigen (GM) and traditional microbiological tests (BALF fungal culture and smear and lung biopsy histopathology). Ninety-four patients with suspicion of IPA were finally enrolled in our study. Thirty-nine patients were diagnosed with IPA, and 55 patients were non-IPA. There was significance between the IPA and non-IPA groups, such as BALF GM (P < 0.001), history of glucocorticoid use (P = 0.004), and pulmonary comorbidities (P = 0.002), as well as no significance of the other demographic data including age, sex, BMI, history of cigarette, blood GM assay, T-SPOT.TB, and NEUT#/LYMPH#. The sensitivity of the BALF mNGS was 92.31%, which was higher than that of the traditional tests or the GM assays. The specificity of BALF mNGS was 92.73%, which was relatively similar to that of the traditional tests. The AUC of BALF mNGS was 0.925, which presented an excellent performance compared with other traditional tests or GM assays. Our study demonstrated the important role of BALF detection by the mNGS platform for pathogen identification in IPA patients with non-neutropenic states, which may provide an optimal way to diagnose suspected IPA disease

A Molecular Switch between Mammalian MLL Complexes Dictates Response to Menin-MLL Inhibition

Menin interacts with oncogenic MLL1-fusion proteins, and small molecules that disrupt these associations are in clinical trials for leukemia treatment. By integrating chromatin-focused and genome-wide CRISPR screens with genetic, pharmacologic, and biochemical approaches, we discovered a conserved molecular switch between the MLL1-Menin and MLL3/4-UTX chromatin-modifying complexes that dictates response to Menin-MLL inhibitors. MLL1-Menin safeguards leukemia survival by impeding the binding of the MLL3/4-UTX complex at a subset of target gene promoters. Disrupting the Menin-MLL1 interaction triggers UTX-dependent transcriptional activation of a tumor-suppressive program that dictates therapeutic responses in murine and human leukemia. Therapeutic reactivation of this program using CDK4/6 inhibitors mitigates treatment resistance in leukemia cells that are insensitive to Menin inhibitors. These findings shed light on novel functions of evolutionarily conserved epigenetic mediators like MLL1-Menin and MLL3/4-UTX and are relevant to understand and target molecular pathways determining therapeutic responses in ongoing clinical trials

Three-Dimensional Regularized Focusing Migration: A Case Study from the Yucheng Mining Area, Shandong, China

Gravity migration is a fast imaging technique based on the migration concept to obtain subsurface density distribution. For higher resolution of migration imaging results, we propose a 3D regularized focusing migration method that implements migration imaging of an entire gravity survey with a focusing stabilizer based on regularization theory. When determining the model parameters, the iterative direction is chosen as the conjugate migration direction, and the step size is selected on the basis of the Wolfe–Powell conditions. The model tests demonstrate that the proposed method can improve the resolution and precision of imaging results, especially for blocky structures. At the same time, the method has high computational efficiency, which allows rapid imaging for large-scale gravity data. It also has high stability in noisy conditions. The developed novel method is applied to interpret gravity data collected from the skarn-type iron deposits in Yucheng, Shandong province. Migration results show that the depth of the buried iron ore in this area is 750–1500 m, which is consistent with the drilling data. We also provide recommendations for further mineral exploration in the survey area. This method can be used to complete rapid global imaging of large mining areas and it provides important technical support for exploration of deep, concealed deposits

The Estimation of Magnetite Prospective Resources Based on Aeromagnetic Data: A Case Study of Qihe Area, Shandong Province, China

In the Qihe area, the magnetic anomalies caused by deep and concealed magnetite are weak and compared with ground surveys, airborne surveys further weaken the signals. Moreover, the magnetite in the Qihe area belongs to a contact-metasomatic deposit, and the magnetic anomalies caused by the magnetite and its mother rock overlap and interweave. Therefore, it is difficult to directly delineate the target areas of magnetite according to the measured aeromagnetic maps in Qihe or similar areas, let alone estimate prospective magnetite resources. This study tried to extract magnetite-caused anomalies from aeromagnetic data by using high-pass filtering. Then, a preliminary estimation of magnetite prospective resources was realized by the 3D inversion of the extracted anomalies. In order to improve the resolution and accuracy of the inversion results, a combined model-weighting function was proposed for the inversion. Meanwhile, the upper and lower bounds and positive and negative constraints were imposed on the model parameters to further improve the rationality of the inversion results. A theoretical model with deep and concealed magnetite was established. It demonstrated the feasibility of magnetite-caused anomaly extraction and magnetite prospective resource estimation. Finally, the magnetite-caused anomalies were extracted from the measured aeromagnetic data and were consistent with known drilling information. The distribution of underground magnetic bodies was obtained by the 3D inversion of extracted anomalies, and the existing drilling data were used to delineate the volume of magnetite. In this way, the prospective resources of magnetite in Qihe area were estimated

Harmonizing atmospheric ozone column concentrations over the Tibetan Plateau from 2005 to 2022 using OMI and Sentinel-5P TROPOMI: A deep learning approach

Atmospheric ozone plays a pivotal role in Earth's climate system, influencing solar radiation absorption in the stratosphere and regulating ultraviolet light reaching the surface. Accurate monitoring of ozone concentration is crucial for environmental assessments, air quality monitoring, and climate change studies. The Ozone Monitoring Instrument (OMI) and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) provide valuable data for such monitoring. While OMI offers a long data record since 2004, but its effectiveness is hindered by its limitations in spatial resolution and signal-to-noise ratio, stemming from satellite hardware and retrieval algorithms. Sentinel-5P TROPOMI provides higher spatial resolution and improved signal-to-noise ratio, nevertheless, data record from it is rather short. Harmonizing these two datasets by taking the best use of their specific advantages is essential for creating a comprehensive and accurate atmospheric ozone concentration dataset. To maximize the advantages of these multi-source data products, our method utilizes a neural network to learn the mapping relationship between OMI and Sentinel-5P TROPOMI ozone column concentration products, constructing a harmonized model that optimizes the spatial and temporal sequence of historical OMI ozone column concentrations while considering topographic factors. The reconstructed ozone column concentration product is a long time series with the high spatial resolution and accuracy characteristics of Sentinel-5P TROPOMI. This research leverages powerful nonlinear modeling and spatial feature mapping capabilities based on deep learning networks to create a harmonized dataset of atmospheric ozone column concentrations, offering a comprehensive understanding of ozone distribution across the Tibetan Plateau. This dataset not only improves accuracy and precision in ozone concentration measurements but also facilitates in-depth analysis of local ozone variations, providing reliable dataset for scientific investigations into the atmospheric environment. The complete dataset is openly accessible at https://doi.org/10.5281/zenodo.10430751

An Efficient and Economical Combination of Exploration Methods for Pb-Zn Polymetallic Skarn Deposits: A Case Study of the Periphery of Hetaoping Deposit, Yunnan Province, China

The Hetaoping ore district in Baoshan City, Yunnan Province, is one of the major localities of Pb-Zn polymetallic skarn deposits in China, where geophysical and geochemical surveys play an important role in exploring Pb-Zn polymetallic mineral resources. Based on the exploration and prospecting carried out at the periphery of the Hetaoping Pb-Zn polymetallic deposit, this study proposed an aero-ground joint exploration method to determine the metallogenic model of distal skarns in the Hetaoping ore district, achieving ideal prospecting results. The steps of this method are as follows. First, the locations of ore-induced anomalies were determined using high-amplitude aeromagnetic anomalies. Then, the ore-induced anomalies were determined to be anomalies of Pb-Zn polymetallic deposits through geochemical surveys of soil samples and ground geophysical surveys. Based on these data, a quantitative analysis and metallogenic potential assessment of ore bodies and their surrounding rocks were conducted using the interactive 2.5D magnetic inversion. In addition, the 3D inversion of regional gravity data was also performed in order to determine the spatial location of the deep magma chamber. Accordingly, the metallogenic geological process in this area was analyzed by determining the spatial morphology of the deep magma chamber, and a prospecting model of the Pb-Zn polymetallic deposits was finally built. The results show that the aero-ground joint exploration method, which first conducts a rapid scanning survey using the aeromagnetic method and then locates, distinguishes, and assesses significant aeromagnetic anomalies by combining comprehensive verification means such as ground geophysical, geochemical, and geological surveys, is efficient and economical. This study will guide regional metallogenic research and the exploration and prospecting of Pb-Zn polymetallic deposits

A Near-Real-Time Flood Detection Method Based on Deep Learning and SAR Images

Owning to the nature of flood events, near-real-time flood detection and mapping is essential for disaster prevention, relief, and mitigation. In recent years, the rapid advancement of deep learning has brought endless possibilities to the field of flood detection. However, deep learning relies heavily on training samples and the availability of high-quality flood datasets is rather limited. The present study collected 16 flood events in the Yangtze River Basin and divided them into three categories for different purpose: training, testing, and application. An efficient methodology of dataset-generation for training, testing, and application was proposed. Eight flood events were used to generate strong label datasets with 5296 tiles as flood training samples along with two testing datasets. The performances of several classic convolutional neural network models were evaluated with those obtained datasets, and the results suggested that the efficiencies and accuracies of convolutional neural network models were obviously higher than that of the threshold method. The effects of VH polarization, VV polarization, and the involvement of auxiliary DEM on flood detection were investigated, which indicated that VH polarization was more conducive to flood detection, while the involvement of DEM has a limited effect on flood detection in the Yangtze River Basin. Convolutional neural network trained by strong datasets were used in near-real-time flood detection and mapping for the remaining eight flood events, and weak label datasets were generated to expand the flood training samples to evaluate the possible effects on deep learning models in terms of flood detection and mapping. The experiments obtained conclusions consistent with those previously made on experiments with strong datasets

Downscaling and Calibration Analysis of Precipitation Data in the Songhua River Basin Using the GWRK Model and Rain Gauges

Obtaining high-quality precipitation data with both high spatial and temporal resolution is imperative for hydrological and meteorological research. However, the coarse resolution and uncertain data quality of most satellite data, coupled with sparse rain gauge station (RGS), limit their direct applicability in scientific research. Downscaling satellite data, particularly in conjunction with RGS, proves to be an effective approach to overcome this challenge. In this study, we utilize the geographically weighted regression kriging model to downscale global precipitation measurement IMERG monthly precipitation data from 2001 to 2020. Leveraging spatially heterogeneous relationships with digital elevation model, slope, land surface temperature, and soil moisture in the Songhua River Basin in Northeast China, we enhance the spatial resolution from 0.1° to 1 km, initially achieving a 1.4% increase in data accuracy, with a CC value of 0.966. Subsequently, employing the daily fraction method, the downscaled precipitation data are disaggregated to the daily scale and calibrated by merging RGS using the geographical difference analysis method. The outcome is high-quality daily precipitation data with both high spatial resolution and accuracy (CC = 0.818, RMSE = 3.188, and ME = 0.086). An analysis of the annual variation of precipitation in the Songhua River Basin over the past two decades reveals an increasing trend. Spatially, the average annual precipitation variation rate in the basin increases from the middle to both ends, with the increasing trend gradually decreasing from south to north. The proposed approach provides a practical solution for enhancing the spatiotemporal scale of satellite data, improving data quality, and addressing the sparse distribution of RGS

Modeling hydrological consequences of 21st-Century climate and land use/land cover changes in a mid-high latitude watershed

The Naoli River Basin (NRB), a pivotal agricultural production area in China, is poised to undergo substantial impacts on water resources due to projected climate and land use/cover (LULC) changes. Despite its significance in the context of China's expanding farmland construction in the NRB, there exists limited research on the potential repercussions of future shifts in runoff, soil water content (SWC), and evapotranspiration (ET) on crop productivity and water availability (both in terms of quantity and timing). This study employs future LULC maps and an ensemble of ten CMIP6 Global Climate Models (GCMs) across three scenarios to drive the well-calibrated distributed hydrological model, ESSI-3. The objective of present study is aimed on projecting hydrological consequences under climate and land use/land cover changes in near-term (2026–2050), middle-term (2051–2075), and far-term (2076–2100) future in comparison to the baseline period of 1990–2014. Results consistently indicate an increase trend in annual average ET, runoff, and SWC in the NRB across all three future periods under the three SSP scenarios. LULC changes emerge as the primary driver influencing regional hydrological processes in the near future. Notably, under high-emission scenarios, monthly runoff and SWC are projected to significantly increase in March but decrease in April during the middle and far future periods compared to the baseline. This shift is attributed to the anticipated warming of winter and spring, leading to a transition in peak snowmelt from April to March. Concurrently, the expansion of cropland intensifies crop evapotranspiration demand, potentially exacerbating water stress during the early stages of crop growth in April. The findings underscore the importance of addressing the substantial impacts of climate change and land use planning on regional water cycling processes. Early planning to mitigate water shortages during the initial stage of future crop growth is crucial for ensuring food security and managing water-related challenges in the NRB and neighboring mid-high latitude regions