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إن الرموز الصينية هي أحد أهم معالم اللغة الصينية فهي تعد بالأمر الغريب على كل من هو مبتدئ فى دراسة اللغة ونظرا لكثرة الرموز والتي يتجاوز عددها أكثر من 6000 رمز وهذا يجعل عملية حفظ الرموز شبه مستحيلة ولكن هذه السلسلة الفريدة والأولى من نوعها المترجمة إلى العربية التي تحكي قصة تطور الرمز بدءا من نقشه على عظام السلاحف والألواح النحاسية وبعد انتهائك من هذه السلسلة لن يقف أمامك أي رمز وستتمكن من حفظ وكتابه الرموز بشكل محترف.

Deep neural networks that learn from climate reanalysis data have produced skillful weather forecasts within ten days. However, it is still a great challenge for dynamic models to predict soil moisture, droughts, and other extreme events with lead times beyond two weeks. Here, we combine a recursive deep learning model (namely RISE-UNet) and subseasonal forecasts from dynamic models and achieve skillful forecasts of root zone soil moisture up to four weeks in advance. Our hybrid model, combining RISE-UNet and dynamic model forecasts, outperforms reanalysis-driven RISE-UNet models, while both methods show significantly higher performance than the latest European Centre for Medium-Range Weather Forecasts (ECMWF) and Global Ensemble Forecast System (GEFS) dynamic models, the postprocessed ECMWF or GEFS subseasonal forecasts by RISE-UNet, or ensemble model output statistics. The hybrid model shows skill in predicting flash droughts, which is higher than ECMWF and GEFS models in most cases, as demonstrated for major events in the United States, China, and Australia. The forecast skill of the hybrid modeling approach from weeks three to four is mainly due to the inclusion of the first two-week dynamic model forecasts and antecedent root zone soil moisture reanalysis. Our results indicate that combining deep learning with dynamic model forecasts can substantially improve the skill of subseasonal predictions beyond two weeks, particularly for root zone soil moisture and flash drought events. This study presents a hybrid approach that combines deep learning with dynamic models for soil moisture forecast. This hybrid approach significantly improves soil moisture and flash drought forecasts beyond two weeks, offering a promising path to earlier warning of climate extremes.

Detecting and quantifying the global teleconnections with flash droughts (FDs) and understanding their causal relationships is crucial to improve their predictability. This study employs causal effect networks (CENs) to explore the global predictability sources of subseasonal soil moisture FDs in three regions of the United States (US): upper Mississippi, South Atlantic Gulf (SAG), and upper and lower Colorado river basins. We analyzed the causal relationships of FD events with global 2‐m air temperature, sea surface temperature, water deficit (precipitation minus evaporation), and geopotential height at 500 hPa at the weekly timescale over the warm season (April to September) from 1982 to 2018. CENs revealed that the Indian Ocean Dipole, Pacific North Atlantic patterns, Bermuda high‐pressure system, and teleconnection patterns via Rossby wave train and jet streams strongly influence FDs in these regions. Moreover, a strong link from South America suggests that atmospheric circulation forcings could affect the SAG through the low‐level atmospheric flow, reducing inland moisture transport, and leading to a precipitation deficit. Machine learning utilizing the identified causal regions and factors can well predict major FD events up to 4 weeks in advance, providing useful insights for improved subseasonal forecasting and early warnings. Plain Language Summary This study investigates the global factors affecting flash droughts (FDs) in the upper Mississippi, South Atlantic Gulf, and upper and lower Colorado river basins in the United States. Using causal effect networks, the research identifies key global influences, such as the Indian Ocean Dipole, Pacific North Atlantic patterns, and Bermuda high‐pressure system, which affect FDs through atmospheric circulation and jet streams. Findings show that these factors, along with local and remote climate processes, can predict FDs up to 4 weeks in advance. Machine learning models utilizing these climate variables effectively forecast FDs events. The study highlights the importance of large‐scale climate oscillations and teleconnections in predicting FDs, offering useful insights for improving early warnings and climate risk management. Key Points Causal discovery framework identified local and remote regions over the globe causing U.S. Flash droughts (FDs) Machine learning with climate variables over the discovered regions can well predict U.S. FDs up to 4 weeks in advance The discovered regions are consistent with the known sources of predictability but also reveal potentially new sources of predictability

This study developed and evaluated 30‐m daily evapotranspiration (ET) estimates using the Priestley‐Taylor Jet Propulsion Laboratory (PT‐JPL) model with ECOSTRESS, Moderate MODIS, harmonized Landsat Sentinel‐2 (HLS) imagery, ERA5‐Land reanalysis, and eddy covariance measurements. The new daily 30‐m ET showed significantly improved performance (overall, r = 0.8, RMSE = 1.736, KGE = 0.466) at 145 EC sites over contiguous United States compared to the current 70‐m ECOSTRESS ET (overall, r = 0.485, RMSE = 4.696, KGE = −0.841). A deep neural network postprocessing model trained with ET measurements from EC sites further improved the performance on test sites that were not used for model training (overall, r = 0.842, RMSE = 0.88, KGE = 0.792). The 30‐m ET estimation biases were significantly related to the biases in the upwelling longwave (RUL) and downwelling shortwave radiation (RDS) inputs, with ET estimates driven by MODIS radiation showing higher biases compared to those driven by ERA5‐Land radiation. The error diagnosis using random forest indicates that ET biases tend to be larger under higher ET estimates, and RUL and RDS were the primary contributors to the high bias at the higher ET ranges, with partial dependence plots revealing that the estimation biases tend to be higher under more humid environment, denser vegetation covers, and high net radiation conditions. In conclusion, higher spatial resolution satellite imagery of vegetation characteristics and higher temporal resolution radiation data, combined with continent‐wide EC measurements and deep learning, provided substantial added value for improving ET estimations at the field scale (30‐m). Key Points We developed and evaluated 30‐m evapotranspiration (ET) estimates driven by ECOSTRESS, MODIS, harmonized Landsat Sentinel‐2, and ERA5‐Land data The new 30‐m ET estimates showed significantly improved performance compared to the current 70‐m ECOSTRESS ET over contiguous United States Deep neural network with eddy covariance measurements further improved 30‐m ET estimates at unmeasured locations

The outbreak of COVID-19 has become a worldwide pandemic. The pathogenesis of this infectious disease and how it differs from other drivers of pneumonia is unclear. Here we analyze urine samples from COVID-19 infection cases, healthy donors and non-COVID-19 pneumonia cases using quantitative proteomics. The molecular changes suggest that immunosuppression and tight junction impairment occur in the early stage of COVID-19 infection. Further subgrouping of COVID-19 patients into moderate and severe types shows that an activated immune response emerges in severely affected patients. We propose a two-stage mechanism of pathogenesis for this unusual viral infection. Our data advance our understanding of the clinical features of COVID-19 infections and provide a resource for future mechanistic and therapeutics studies. How COVID-19 pathology differs from other drivers of pneumonia is unclear. Here the authors analyze urine from patients with COVID-19 and identify an immunosuppressive protein expression pattern that is distinct from the pattern in healthy individuals or patients with non-COVID-19 pneumonia.

A patient's infectivity is determined by the presence of the virus in different body fluids, secretions, and excreta. The persistence and clearance of viral RNA from different specimens of patients with 2019 novel coronavirus disease (COVID-19) remain unclear. This study analyzed the clearance time and factors influencing 2019 novel coronavirus (2019-nCoV) RNA in different samples from patients with COVID-19, providing further evidence to improve the management of patients during convalescence. The clinical data and laboratory test results of convalescent patients with COVID-19 who were admitted to from January 20, 2020 to February 10, 2020 were collected retrospectively. The reverse transcription polymerase chain reaction (RT-PCR) results for patients' oropharyngeal swab, stool, urine, and serum samples were collected and analyzed. Convalescent patients refer to recovered non-febrile patients without respiratory symptoms who had two successive (minimum 24 h sampling interval) negative RT-PCR results for viral RNA from oropharyngeal swabs. The effects of cluster of differentiation 4 (CD4)+ T lymphocytes, inflammatory indicators, and glucocorticoid treatment on viral nucleic acid clearance were analyzed. In the 292 confirmed cases, 66 patients recovered after treatment and were included in our study. In total, 28 (42.4%) women and 38 men (57.6%) with a median age of 44.0 (34.0-62.0) years were analyzed. After in-hospital treatment, patients' inflammatory indicators decreased with improved clinical condition. The median time from the onset of symptoms to first negative RT-PCR results for oropharyngeal swabs in convalescent patients was 9.5 (6.0-11.0) days. By February 10, 2020, 11 convalescent patients (16.7%) still tested positive for viral RNA from stool specimens and the other 55 patients' stool specimens were negative for 2019-nCoV following a median duration of 11.0 (9.0-16.0) days after symptom onset. Among these 55 patients, 43 had a longer duration until stool specimens were negative for viral RNA than for throat swabs, with a median delay of 2.0 (1.0-4.0) days. Results for only four (6.9%) urine samples were positive for viral nucleic acid out of 58 cases; viral RNA was still present in three patients' urine specimens after throat swabs were negative. Using a multiple linear regression model (F = 2.669, P = 0.044, and adjusted R = 0.122), the analysis showed that the CD4+ T lymphocyte count may help predict the duration of viral RNA detection in patients' stools (t = -2.699, P = 0.010). The duration of viral RNA detection from oropharyngeal swabs and fecal samples in the glucocorticoid treatment group was longer than that in the non-glucocorticoid treatment group (15 days vs. 8.0 days, respectively; t = 2.550, P = 0.013) and the duration of viral RNA detection in fecal samples in the glucocorticoid treatment group was longer than that in the non-glucocorticoid treatment group (20 days vs. 11 days, respectively; t = 4.631, P < 0.001). There was no statistically significant difference in inflammatory indicators between patients with positive fecal viral RNA test results and those with negative results (P > 0.05). In brief, as the clearance of viral RNA in patients' stools was delayed compared to that in oropharyngeal swabs, it is important to identify viral RNA in feces during convalescence. Because of the delayed clearance of viral RNA in the glucocorticoid treatment group, glucocorticoids are not recommended in the treatment of COVID-19, especially for mild disease. The duration of RNA detection may relate to host cell immunity.

Drought is one of the abiotic stresses controlling plant function and ecological stability. In the context of climate change, drought is predicted to occur more frequently in the future. Despite numerous attempts to clarify the overall effects of drought stress on the growth and physiological processes of plants, a comprehensive evaluation on the impacts of drought stress on biomass allocation, especially on reproductive tissues, remains elusive. We conducted a meta‐analysis by synthesizing 164 published studies to elucidate patterns of plant biomass allocation in relation to drought stress. Results showed that drought significantly increased the fraction of root mass but decreased that of stem, leaf, and reproductive mass. Roots of herbaceous plants were more sensitive to drought than woody plants that reduced reproductive allocation more sharply than the former. Relative to herbaceous plants, drought had a more negative impact on leaf mass fraction of woody plants. Among the herbaceous plants, roots of annuals responded to drought stress more strongly than perennial herbs, but their reproductive allocation was less sensitive to drought than the perennial herbs. In addition, cultivated and wild plants seemed to respond to drought stress in a similar way. Drought stress did not change the scaling exponents of the allometric relationship between different plant tissues. These findings suggest that the allometric partitioning theory, rather than the optimal partitioning theory, better explains the drought‐induced changes in biomass allocation strategies. Drought significantly increased the fraction of root mass but decreased the mass fractions of stem, leaf, and reproductive parts. Roots of herbaceous plants are more sensitive to drought than woody plants that reduced reproductive allocation more sharply than the former. Drought stress did not alter the scaling exponents of the allometric relationship between different plant tissues.

Systematic biases and coarse resolutions are major limitations of current precipitation datasets. Many deep learning (DL)-based studies have been conducted for precipitation bias correction and downscaling. However, it is still challenging for the current approaches to handle complex features of hourly precipitation, resulting in the incapability of reproducing small-scale features, such as extreme events. This study developed a customized DL model by incorporating customized loss functions, multitask learning and physically relevant covariates to bias correct and downscale hourly precipitation data. We designed six scenarios to systematically evaluate the added values of weighted loss functions, multitask learning, and atmospheric covariates compared to the regular DL and statistical approaches. The models were trained and tested using the Modern-era Retrospective Analysis for Research and Applications version 2 (MERRA2) reanalysis and the Stage IV radar observations over the northern coastal region of the Gulf of Mexico on an hourly time scale. We found that all the scenarios with weighted loss functions performed notably better than the other scenarios with conventional loss functions and a quantile mapping-based approach at hourly, daily, and monthly time scales as well as extremes. Multitask learning showed improved performance on capturing fine features of extreme events and accounting for atmospheric covariates highly improved model performance at hourly and aggregated time scales, while the improvement is not as large as from weighted loss functions. We show that the customized DL model can better downscale and bias correct hourly precipitation datasets and provide improved precipitation estimates at fine spatial and temporal resolutions where regular DL and statistical methods experience challenges.

Plant carbon (C) content is one of the most important plant traits and is critical to the assessment of global C cycle and ecological stoichiometry; however, the global variations in plant C content remain poorly understood. In this study, we conducted a global analysis of the plant C content by synthesizing data from 4318 species to document specific values and their variation of the C content across plant organs and life forms. Plant organ C contents ranged from 45.0 % in reproductive organs to 47.9 % in stems at global scales, which were significantly lower than the widely employed canonical value of 50 %. Plant C content in leaves (global mean of 46.9 %) was higher than that in roots (45.6 %). Across life forms, woody plants exhibited higher C content than herbaceous plants. Conifers, relative to broad-leaved woody species, had higher C content in roots, leaves, and stems. Plant C content tended to show a decrease with increasing latitude. The life form explained more variation of the C content than climate. Our findings suggest that specific C content values of different organs and life forms developed in our study should be incorporated into the estimations of regional and global vegetation biomass C stocks.

Pedestrian inertial navigation is a pivotal technology for achieving seamless indoor and outdoor positioning. Traditional methods based on the Extended Kalman Filter (EKF) suffer from cumulative errors induced by inertial measurement unit (IMU) noise, which severely degrade the accuracy of pedestrian trajectory estimation over long durations. To address this critical limitation, a post-processing trajectory optimization approach for pedestrian inertial navigation based on the Birkhoff pseudospectral method is proposed in this paper. Leveraging the initial attitude and position estimates derived from the Zero-Velocity Update (ZUPT) technique and the EKF framework, the proposed method first parameterizes continuous-time acceleration measurements by adopting Chebyshev nodes as collocation points, and then formulates and solves the trajectory optimization problem via a Birkhoff pseudospectral framework, which effectively suppresses noise interference from the IMU accelerometer. Simulation experiments validate the superior noise suppression capability of the proposed algorithm. Furthermore, physical experiments conducted with a foot-mounted IMU demonstrate that the final position error is reduced by approximately 90% in comparison with the traditional EKF-based method.