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Cloud detection is an indispensable step in satellite remote sensing of cloud properties and objects under the influence of cloud occlusion. Nevertheless, interfering targets such as snow and haze pollution are easily misjudged as clouds for most of the current algorithms. Hence, a robust cloud detection algorithm is urgently needed, especially for regions with high latitudes or severe air pollution. This paper demonstrated that the passive satellite detector Advanced Geosynchronous Radiation Imager (AGRI) onboard the FY-4A satellite has a great possibility to misjudge the dense aerosols in haze pollution as clouds during the daytime, and constructed an algorithm based on the spectral information of the AGRI’s 14 bands with a concise and high-speed calculation. This study adjusted the previously proposed cloud mask rectification algorithm of Moderate-Resolution Imaging Spectroradiometer (MODIS), rectified the MODIS cloud detection result, and used it as the accurate cloud mask data. The algorithm was constructed based on adjusted Fisher discrimination analysis (AFDA) and spectral spatial variability (SSV) methods over four different underlying surfaces (land, desert, snow, and water) and two seasons (summer and winter). This algorithm divides the identification into two steps to screen the confident cloud clusters and broken clouds, which are not easy to recognize, respectively. In the first step, channels with obvious differences in cloudy and cloud-free areas were selected, and AFDA was utilized to build a weighted sum formula across the normalized spectral data of the selected bands. This step transforms the traditional dynamic-threshold test on multiple bands into a simple test of the calculated summation value. In the second step, SSV was used to capture the broken clouds by calculating the standard deviation (STD) of spectra in every 3 × 3-pixel window to quantify the spectral homogeneity within a small scale. To assess the algorithm’s spatial and temporal generalizability, two evaluations were conducted: one examining four key regions and another assessing three different moments on a certain day in East China. The results showed that the algorithm has an excellent accuracy across four different underlying surfaces, insusceptible to the main interferences such as haze and snow, and shows a strong detection capability for broken clouds. This algorithm enables widespread application to different regions and times of day, with a low calculation complexity, indicating that a new method satisfying the requirements of fast and robust cloud detection can be achieved.

This study validated the robust performances of the recently proposed comprehensive landslide susceptibility index model (CLSI) for landslide susceptibility mapping (LSM) by comparing it to the logistic regression (LR) and the analytical hierarchy process information value (AHPIV) model. Zhushan County in China, with 373 landslides identified, was used as the study area. Eight conditioning factors (lithology, slope structure, slope angle, altitude, distance to river, stream power index, slope length, distance to road) were acquired from digital elevation models (DEMs), field survey, remote sensing imagery, and government documentary data. Results indicate that the CLSI model has the highest accuracy and the best classification ability, although all three models can produce reasonable landslide susceptibility (LS) maps. The robust performance of the CLSI model is due to its weight determination by a back-propagation neural network (BPNN), which successfully captures the nonlinear relationship between landslide occurrence and the conditioning factors.

We investigate the pulsating behavior of KIC 2857323 using high-precision observations from the Kepler mission. Fourier analysis of 4 yr time-series data reveals five independent frequencies for the light variation. Among them, two strong frequencies f 1 and f 3 with a period ratio of 0.774 identify this star as a double-mode (i.e., the fundamental mode F0 and first overtone mode F1) high-amplitude δ Scuti star (HADS). Seismic modeling using the two radial modes F0 and F1 indicates that KIC 2857323 is a main-sequence star with mass M = 1.78 ± 0.02 M ⊙ and metallicity Z from 0.009 to 0.012. We analyze the phase and amplitude variations of F0 and F1 using the phase modulation method and find that the first overtone mode F1 shows a slow decline in amplitude. We discuss several possible causes for the amplitude variation and speculate that the amplitude decline in this star may be due to pulsation energy loss. We note that KIC 2857323 is the first double-mode HADS to show amplitude decline and warrants further study to ascertain its nature.

Due to the spatial variability of hydraulic properties, probabilistic slope seepage analysis becomes necessary. This study conducts a probabilistic analysis of slope seepage under rainfall, considering the spatial variability of saturated hydraulic conductivity. Through this, both the commonly used Monte Carlo simulation method and the proposed first-order stochastic moment approach are tested and compared. The results indicate that the first-order analysis approach is effective and applicable to the study of flow processes in a slope scenario. It is also capable of obtaining statistics such as mean and variance with a high enough accuracy. Using this approach, higher variabilities in the pressure head and the fluctuation of the phreatic surface in the slope are found with a higher value of the correlation length of the saturated hydraulic conductivity. The Monte Carlo simulation is found to be time-consuming: at least 10,000 realizations are required to reach convergence, and the number of realizations needed is sensitive to the grid density. A coarser grid case requires more realizations for convergence. If the number of realizations is not enough, the results are unreliable. Compared with Monte Carlo simulation, the accuracy of the first-order stochastic moment analysis is generally satisfied when the variance and the correlation length of the saturated hydraulic conductivity are not too large. This study highlights the applicability of the proposed first-order stochastic moment analysis approach in the slope scenario.

An adaptive sampling approach is proposed, which can sample spatially varying shear strength parameters efficiently to reduce uncertainty in the slope stability analysis. This approach employs a limit equilibrium model and stochastic conditional methodology to determine the likely sampling locations. Karhunen-Loève expansion is used to conduct the conditional Monte Carlo simulation. A first-order analysis is also proposed to ease the computational burden associated with Monte Carlo simulation. These approaches are then tested using borehole data from a field site. Results indicate that the proposed adaptive sampling approach is an effective and efficient sampling scheme for reducing uncertainty in slope stability analysis.

Bacterial plant pathogens pose a serious threat to worldwide crop yields and cause widespread food insecurity. One common approach to limit pathogen proliferation on critical crops is to genetically engineer cultivars that harbour resistance genes capable of recognising and responding to critical bacterial virulence factors like type III secreted effectors. Unfortunately, these resistance barriers are often overcome by pathogen evolution within just a few seasons. In this study, we explore the evolutionary mechanisms that enable pathogens to overcome plant resistance by leveraging two Pseudomonas syringae pv. maculicola strains that differ in their ability to cause disease on the model host Arabidopsis thaliana. We first characterise the molecular basis of the adaptation that enabled the P. syringae pv. maculicola PmaES4326 to overcome rps5‐mediated resistance through a direct modification to its hopAR1 effector. We then show that through in planta evolution, the initially nonpathogenic strain P. syringae pv. maculicola PmaYM7930 can rapidly adapt to overcome rps5‐mediated resistance via low‐frequency mutations that do not involve direct modifications to hopAR1. This result was especially surprising because hopAR1 is known to be associated with mobile genetic elements that enable increased evolutionary plasticity. The rapid ability of P. syringae to overcome effector‐triggered immunity without direct modifications to hopAR1 reveals that the genetic mechanisms enabling pathogens to overcome host resistance are more diverse than is currently recognised. This result has important implications for the development of more stably resistant crops that can resist various forms of pathogen evolution. Pseudomonas syringae pv. maculicola rapidly adapts to overcome effector‐triggered immunity during short‐term experimental evolution via diverse genetic mechanisms.

We present the first catalogue of double-mode and multi-mode high amplitude \\(\\) Scuti star (HADS) in the Galaxy. The catalogue contains source name, coordinates, radial modes such as the fundamental (F, period P0), first-overtone (1O, period P1), second-overtone (2O, period P2), and the third-overtone (3O, period P3) if available, period ratios, magnitude, and the relevant literature. Totally, 155 sources were collected until March 2021, in which 142 HADS with double-mode (132 with F and 1O, and 10 with 1O and 2O), 11 triple-mode, and 2 quadruple-mode. Statistical analysis shows clear features: P0 lies in a range of 0.05 days to 0.175 days (sample: 132 double-mode HADS pulsating in F and 1O); P1/P0 lies in a range of 0.761 \\(-\\) 0.787 (sample: 142 with P0 and P1), in which about 90\\% in 0.765 \\(-\\) 0.785, which is wider than previous studies. The Petersen diagram was created with a much larger sample (144 HADS with P0 and P1) and we find that stars with periods in 0.05 \\(-\\) 0.1 days scatter largely from the updated linear relation (i.e., Eq.1), the reason of which however needs further investigation. Particularly, we discover that the ratio P2/P1 (sample: 21 HADS with P1 and P2) equals 0.802\\(\\)0.004, which could be viewed as a possible indicator to identify the modes 1O and 2O for multi-mode HADS. In addition, several unusual stars are pointed out, which may need more attention to their pulsations and stellar parameters in the future.

Background Lung ischemia–reperfusion (I/R) injury is a common clinical pathology associated with high mortality. The pathophysiology of lung I/R injury involves ferroptosis and elevated protein O-GlcNAcylation levels, while the effect of O-GlcNAcylation on lung I/R injury remains unclear. This research aimed to explore the effect of O-GlcNAcylation on reducing ferroptosis in pulmonary epithelial cells caused by I/R. Results First, we identified O-GlcNAc transferase 1 (Ogt1) as a differentially expressed gene in lung epithelial cells of acute lung injury/acute respiratory distress syndrome (ALI/ARDS) patients, using single-cell sequencing, and Gene Ontology analysis (GO analysis) revealed the enrichment of the ferroptosis process. We found a time-dependent dynamic alteration in lung O-GlcNAcylation during I/R injury. Proteomics analysis identified the differentially expressed proteins enriched in ferroptosis and multiple redox-related pathways based on KEGG annotation. Thus, we generated Ogt1-conditional knockout mice and found that Ogt1 deficiency aggravated ferroptosis, as evidenced by lipid reactive oxygen species (lipid ROS), malondialdehyde (MDA), Fe 2+ , as well as alterations in critical protein expression glutathione peroxidase 4 (GPX4) and solute carrier family 7 member 11 (SLC7A11). Consistently, we found that elevated O-GlcNAcylation inhibited ferroptosis sensitivity in hypoxia/reoxygenation (H/R) injury-induced TC-1 cells via O-GlcNAcylated NF-E2-related factor-2 (Nrf2). Furthermore, both the chromatin immunoprecipitation (ChIP) assay and the dual-luciferase reporter assay indicated that Nrf2 could bind with translation start site (TSS) of glucose-6-phosphate dehydrogenase (G6PDH) and promote its transcriptional activity. As an important rate-limiting enzyme in the pentose phosphate pathway (PPP), elevated G6PDH provided a mass of nicotinamide adenine dinucleotide phosphate (NADPH) to improve the redox state of glutathione (GSH) and eventually led to ferroptosis resistance. Rescue experiments proved that Nrf2 knockdown or Nrf2-T334A (O-GlcNAcylation site) mutation abolished the protective effect of ferroptosis resistance. Conclusions In summary, we revealed that O-GlcNAcylation could protect against I/R lung injury by reducing ferroptosis sensitivity via the Nrf2/G6PDH pathway. Our work will provide a new basis for clinical therapeutic strategies for pulmonary ischemia–reperfusion-induced acute lung injury.

The Changle goose (CLG), a Chinese indigenous breed, is celebrated for its adaptability, rapid growth, and premium meat quality. Despite its agricultural value, the exploration of its genomic attributes has been scant. Our study entailed whole‐genome resequencing of 303 geese across CLG and five other Chinese breeds, revealing distinct genetic diversity metrics. We discovered significant migration events from Xingguo gray goose to CLG and minor gene flow between them. We identified genomic regions through selective sweep analysis, correlating with CLG's unique traits. An elevated inbreeding coefficient in CLG, alongside reduced heterozygosity and rare single nucleotide polymorphisms (RSNPs), suggests a narrowed genetic diversity. Genomic regions related to reproduction, meat quality, and growth were identified, with the GATA3 gene showing strong selection signals for meat quality. A non‐synonymous mutation in the Sloc2a1 gene, which is associated with reproductive traits in the CLG, exhibited significant differences in allelic frequency. The roles of CD82, CDH8, and PRKAB1 in growth and development, alongside FABP4, FAF1, ESR1, and AKAP12 in reproduction, were highlighted. Additionally, Cdkal1 and Mfsd14a may influence meat quality. This comprehensive genetic analysis underpins the unique genetic makeup of CLG, providing a basis for its conservation and informed breeding strategies.

Background Standard‐of‐care for warfarin dose titration is conventionally based on physician‐guided drug dosing. This may lead to frequent deviations from target international normalized ratio (INR) due to inter‐ and intra‐patient variability and may potentially result in adverse events including recurrent thromboembolism and life‐threatening hemorrhage. Objectives We aim to employ CURATE.AI, a small‐data, artificial intelligence‐derived platform that has been clinically validated in a range of indications, to optimize and guide warfarin dosing. Patients/methods A personalized CURATE.AI response profile was generated using warfarin dose (inputs) and corresponding change in INR between two consecutive days (phenotypic outputs) and used to identify and recommend an optimal dose to achieve target treatment outcomes. CURATE.AI's predictive performance was then evaluated with a set of metrics that assessed both technical performance and clinical relevance. Results and conclusions In this retrospective study of 127 patients, CURATE.AI fared better in terms of Percentage Absolute Prediction Error and Percentage Prediction Error of 20% compared to other models in the literature. It also had negligible underprediction bias, potentially translating into lower bleeding risk. Modeled potential time in therapeutic range with CURATE.AI was not significantly different from physician‐guided dosing, so it is on‐par yet provides a systematic approach to warfarin dosing, easing the mental‐burden on guesswork by physicians. This study lays the groundwork for the prospective study of CURATE.AI as a clinical decision support system. CURATE.AI may facilitate the effective use of affordable warfarin with a well‐established safety profile, without the need for costly, new oral anticoagulants. This can have significant impact both on the individual and public health.