Effects of Cloud Horizontal Inhomogeneity and Drizzle on Remote Sensing of Cloud Droplet Effective Radius: Case Studies Based on Large-eddy Simulations

This study investigates effects of drizzle and cloud horizontal inhomogeneity on cloud effective radius (re) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS). In order to identify the relative importance of various factors, we developed a MODIS cloud property retrieval simulator based on the combination of large-eddy simulations (LES) and radiative transfer computations. The case studies based on synthetic LES cloud fields indicate that at high spatial resolution (100 m) 3-D radiative transfer effects, such as illumination and shadowing, can induce significant differences between retrievals ofre based on reflectance at 2.1 m (re,2.1) and 3.7 m (re,3.7). It is also found that 3-D effects tend to have stronger impact onre,2.1 than re,3.7, leading to positive difference between the two (re,3.72.1) from illumination and negative re,3.72.1from shadowing. The cancellation of opposing 3-D effects leads to overall reasonable agreement betweenre,2.1 and re,3.7 at high spatial resolution as far as domain averages are concerned. At resolutions similar to MODIS, however, re,2.1 is systematically larger than re,3.7when averaged over the LES domain, with the difference exhibiting a threshold-like dependence on bothre,2.1and an index of the sub-pixel variability in reflectance (H), consistent with MODIS observations. In the LES cases studied, drizzle does not strongly impact reretrievals at either wavelength. It is also found that opposing 3-D radiative transfer effects partly cancel each other when cloud reflectance is aggregated from high spatial resolution to MODIS resolution, resulting in a weaker net impact of 3-D radiative effects onre retrievals. The large difference at MODIS resolution between re,3.7 and re,2.1 for highly inhomogeneous pixels with H 0.4 can be largely attributed to what we refer to as the plane-parallelrebias, which is attributable to the impact of sub-pixel level horizontal variability of cloud optical thickness onre retrievals and is greater for re,2.1 than re,3.7. These results suggest that there are substantial uncertainties attributable to 3-D radiative effects and plane-parallelre bias in the MODIS re,2.1retrievals for pixels with strong sub-pixel scale variability, and theH index can be used to identify these uncertainties

Conditions for super-adiabatic droplet growth after entrainment mixing

Cloud droplet response to entrainment and mixing between a cloud and its environment is considered, accounting for subsequent droplet growth during adiabatic ascent following a mixing event. The vertical profile for liquid water mixing ratio after a mixing event is derived analytically, allowing the reduction to be predicted from the mixing fraction and from the temperature and humidity for both the cloud and environment. It is derived for the limit of homogeneous mixing. The expression leads to a critical height above the mixing level: at the critical height the cloud droplet radius is the same for both mixed and unmixed parcels, and the critical height is independent of the updraft velocity and mixing fraction. Cloud droplets in a mixed parcel are larger than in an unmixed parcel above the critical height, which we refer to as the super-adiabatic growth region. Analytical results are confirmed with a bin microphysics cloud model. Using the model, we explore the effects of updraft velocity, aerosol source in the environmental air, and polydisperse cloud droplets. Results show that the mixed parcel is more likely to reach the super-adiabatic growth region when the environmental air is humid and clean. It is also confirmed that the analytical predictions are matched by the volume-mean cloud droplet radius for polydisperse size distributions. The findings have implications for the origin of large cloud droplets that may contribute to onset of collision-coalescence in warm clouds

ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced MiniGPT-4

In recent years, large language models (LLMs) have made significant progress in natural language processing (NLP), with models like ChatGPT and GPT-4 achieving impressive capabilities in various linguistic tasks. However, training models on such a large scale is challenging, and finding datasets that match the model's scale is often difficult. Fine-tuning and training models with fewer parameters using novel methods have emerged as promising approaches to overcome these challenges. One such model is MiniGPT-4, which achieves comparable vision-language understanding to GPT-4 by leveraging novel pre-training models and innovative training strategies. However, the model still faces some challenges in image understanding, particularly in artistic pictures. A novel multimodal model called ArtGPT-4 has been proposed to address these limitations. ArtGPT-4 was trained on image-text pairs using a Tesla A100 device in just 2 hours, using only about 200 GB of data. The model can depict images with an artistic flair and generate visual code, including aesthetically pleasing HTML/CSS web pages. Furthermore, the article proposes novel benchmarks for evaluating the performance of vision-language models. In the subsequent evaluation methods, ArtGPT-4 scored more than 1 point higher than the current \textbf{state-of-the-art} model and was only 0.25 points lower than artists on a 6-point scale. Our code and pre-trained model are available at \url{https://huggingface.co/Tyrannosaurus/ArtGPT-4}.Comment: 16 page

Risk of infantile atopic dermatitis in neonatal lupus erythematosus: a retrospective cohort study

ObjectivesThe onset and progression of atopic dermatitis (AD) are closely linked to autoimmune status. While AD has been observed in children with neonatal lupus erythematosus (NLE), its relationship with perinatal factors remains unclear. This study aimed to identify early-life risk factors for the development of AD in children with NLE within their first two years of life.MethodsWe conducted a multicenter, retrospective cohort study using electronic medical records and follow-up data from patients in the NLE cohort. Children were categorized into AD and non-AD groups based on whether they developed AD by age two. Univariate and multivariate analyses were performed to compare general and clinical data between the two groups.ResultsAD incidence in NLE patients was 27.27 (21/77). Compared to the non-AD group, the AD group had significantly lower use of oral probiotics and intravenous gamma globulin, but higher rates of small-for-gestational-age (SGA) status, hypocomplementemia, thrombocytopenia, anti-SSA, anti-SSB, double antibody (anti-SSA, anti-SSB) positivity, antibiotic use, and systemic glucocorticoid (GC) treatment. Logistic regression analysis revealed that oral probiotics were a protective factor against AD, while double antibody positivity and systemic GC were risk factors.ConclusionIn children with NLE, oral probiotics were associated with a reduced risk of AD, while double antibody positivity and systemic GC administration significantly increased the risk of AD within the first two years of life. However, the limited sample size in this study warrants further findings

Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by diverse clinical features. EEG biomarkers such as spectral power and functional connectivity have emerged as potential tools for enhancing early diagnosis and understanding of the neural processes underlying ASD. However, existing studies yield conflicting results, necessitating a comprehensive, data-driven analysis. We conducted a retrospective cross-sectional study involving 246 children with ASD and 42 control children. EEG was collected, and diverse EEG features, including spectral power and spectral coherence were extracted. Statistical inference methods, coupled with machine learning models, were employed to identify differences in EEG features between ASD and control groups and develop classification models for diagnostic purposes. Our analysis revealed statistically significant differences in spectral coherence, particularly in gamma and beta frequency bands, indicating elevated long range functional connectivity between frontal and parietal regions in the ASD group. Machine learning models achieved modest classification performance of ROC-AUC at 0.65. While machine learning approaches offer some discriminative power classifying individuals with ASD from controls, they also indicate the need for further refinement

Genetic Evaluation of 114 Chinese Short Stature Children in the Next Generation Era: a Single Center Study

Background/Aims: The genetics of human height is a frequently studied and complex issue. However, there is limited genetic research of short stature. To uncover the subgroup of patients to have higher yield and to propose a simplified diagnostic algorithm in the next generation era. Methods: This study included 114 Chinese children with height SDS ≤ -2.5 and unknown etiology from 2014 to 2015. Target/whole exome sequencing (referred as NGS) and chromosomal microarray analysis (CMA) were performed on the enrolled patients sequentially to identify potential genetic etiologies. The samples solved by NGS and CMA were retrospectively studied to evaluate the clinical pathway of the patients following a standard diagnostic algorithm. Results: In total, a potential genetic etiology was identified in 41 (36%) patients: 38 by NGS (33.3%), two by CMA (1.8%), and an additional one by both (0.9%). There were 46 different variants in 29 genes and 2 pathogenic CNVs identified. The diagnostic yield was significantly higher in patients with facial dysmorphism or skeletal abnormalities than those without the corresponding phenotype (P=0.006 and P=0.009, respectively, Pearson’s χ2 test). Retrospectively study the cohort indicate 83.3% patients eventually would be evaluated by NGS/CMA. Conclusion: This study confirms the utility of high-throughput molecular detection techniques for the etiological diagnosis of undiagnosed short stature and suggests that NGS could be used as a primary diagnostic strategy. Patients with facial dysmorphism and/or skeletal abnormalities are more likely to have a known genetic etiology. Moving NGS forward would simplified the diagnostic algorithm