Abstract. Mineralogical composition drives dust impacts on Earth's climate systems. However, most climate models still use homogeneous dust, without accounting for the temporal and spatial variation in mineralogy. To quantify the radiative impact of resolving dust mineralogy on Earth's climate, we implement and simulate the distribution of dust minerals (i.e., illite, kaolinite, smectite, hematite, calcite, feldspar, quartz, and gypsum) from Claquin et al. (1999) (C1999) and activate their interaction with radiation in the GFDL AM4.0 model. Resolving mineralogy reduces dust absorption compared to the homogeneous dust used in the standard GFDL AM4.0 model that assumes a globally uniform hematite volume content of 2.7 % (HD27). The reduction in dust absorption results in improved agreement with observation-based single-scattering albedo (SSA), radiative fluxes from CERES (the Clouds and the Earth's Radiant Energy System), and land surface temperature from the CRU (Climatic Research Unit) compared to the baseline HD27 model version. It also results in distinct radiative impacts on Earth's climate over North Africa. Over the 19-year (from 2001 to 2019) modeled period during JJA (June–July–August), the reduction in dust absorption in AM4.0 leads to a reduction of over 50 % in net downward radiation across the Sahara and approximately 20 % over the Sahel at the top of the atmosphere (TOA) compared to the baseline HD27 model version. The reduced dust absorption weakens the atmospheric warming effect of dust aerosols and leads to an alteration in land surface temperature, resulting in a decrease of 0.66 K over the Sahara and an increase of 0.7 K over the Sahel. The less warming in the atmosphere suppresses ascent and weakens the monsoon inflow from the Gulf of Guinea. This brings less moisture to the Sahel, which combined with decreased ascent induces a reduction of precipitation. To isolate the effect of reduced absorption compared to resolving spatial and temporal mineralogy, we carry out a simulation where the hematite volume content of homogeneous dust is reduced from 2.7 % to 0.9 % (HD09). The dust absorption (e.g., single-scattering albedo) of HD09 is comparable to that of the mineralogically speciated model on a global mean scale, albeit with a lower spatial variation that arises solely from particle size. Comparison of the two models indicates that the spatial inhomogeneity in dust absorption resulting from resolving mineralogy does not have significant impacts on Earth's radiation and climate, provided there is a similar level of dust absorption on a global mean scale before and after resolving dust mineralogy. However, uncertainties related to emission and distribution of minerals may blur the advantages of resolving minerals to study their impact on radiation, cloud properties, ocean biogeochemistry, air quality, and photochemistry. On the other hand, lumping together clay minerals (i.e., illite, kaolinite, and smectite), but excluding externally mixed hematite and gypsum, appears to provide both computational efficiency and relative accuracy. Nevertheless, for specific research, it may be necessary to fully resolve mineralogy to achieve accuracy.
Chronic kidney disease (CKD) is a common, complex, and heterogeneous disease impacting aging populations. Determining the landscape of disease progression trajectories from midlife to senior age in a real-world context allows us to better understand the progression of CKD, the heterogeneity of progression patterns among the risk population, and the interactions with other clinical conditions like cancers. In this study, we use electronic health records (EHRs) to outline the CKD progression trajectory roadmap for the Wake Forest Baptist Medical Center (WFBMC) patient population. We establish an EHR cohort (n = 79,434) with patients' health status identified by 18 Essential Clinical Indices across 508,732 clinical encounters. We develop the DisEase PrOgression Trajectory (DEPOT) approach to model CKD progression trajectories and individualize clinical decision support. The DEPOT is an evidence-driven, graph-based clinical informatics approach that addresses the unique challenges in longitudinal EHR data by systematically using the graph artificial intelligence (graph-AI) model for representation learning and reverse graph embedding for trajectory reconstruction. Moreover, DEPOT includes a prediction model to assign new patients along the progression trajectory. We successfully establish the EHR-based CKD progression trajectories with DEPOT in the WFUBMC cohort. We annotate the trajectories with clinical features, including kidney function, age, and other indices, including cancer. This CKD progression trajectory roadmap reveals diverse kidney failure pathways associated with different clinical conditions. Specifically, we have identified one high-risk trajectory and two low-risk trajectories. Switching pathways from low-risk trajectories to the high-risk one is associated with accelerated decline in kidney function. On this roadmap, high-risk patients are enriched in the skin and GU cancers, which differs from low-risk patients, suggesting fundamentally different disease progression mechanisms. Overall, the CKD progression trajectory roadmap reveals novel diverse renal failure pathways in type 2 diabetes mellitus and highlights disease progression patterns associated with cancer phenotypes.
Summary Endolysosomes (EL) are known for their role in regulating both intracellular trafficking and proteostasis. EL help facilitate elimination of damaged membrane and cytosolic proteins, protein aggregates, membranous organelles and also play an important role in calcium signalling. Despite the importance of EL, their specific role in cardiovascular disease is not well understood. In particular, it’s unclear how EL contribute to atrial pathology over longer time frames. To shed light on this question, we conducted a comprehensive analysis that involved proteomics, transcriptomics, integrated analysis, electron tomography, western blotting, and enzyme assays. To identify the role of EL in atrial fibrillation (AF), we applied a recently published organelle protein isolation method. We used this method to study biopsies from AF goat model and analyse the EL-specific proteins and pathways involved in this condition. Our results revealed the upregulation of the AMPK pathway and the expression of EL-specific proteins that were not found in whole tissue lysates (TL), including GAA, DYNLRB1, CLTB, SIRT3, CCT2, and muscle-specific HSPB2. We also observed structural anomalies, such as autophago-vacuole formation, irregularly shaped mitochondria, and glycogen deposition, which provide insights into the EL’s contribution to AF and related pathways and molecular mechanisms. Overall, our findings suggest that EL play an important role in the development of AF over longer time frames, and provide a more detailed understanding of the underlying molecular processes involved.
Abstract Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. Through benchmarking with other label transfer methods on a total of 30 single cell omics datasets, scGCN consistently demonstrates superior accuracy on leveraging cells from different tissues, platforms, and species, as well as cells profiled at different molecular layers. scGCN is implemented as an integrated workflow as a python software, which is available at https://github.com/QSong-github/scGCN .
For gaseous tritium concentrations measurement,a comparative study is performed for a self-developed proportional counter and a counter made by LND,regarding the P10 background,high-voltage plateau curve,different concentrations of tritium signal and the residual tritium adsorption in the counter.The results show that the background value of the self-developed proportional counter is slightly higher than the LND proportional counter,the high-voltage plateau of two specifications proportional counter is 1 750~1 95 0 V and 1 70 0~2 10 0 V.Different concentrations of gaseous tritium are measured,through the calibration curve the measured value can be converted to the theory activity value,the self-developed counter can meet the purpose of effective measurement of gaseous tritium.
Understanding organogenesis, disorders, and repairing processes is particularly important for understanding the disease occurrence and developing treatment approaches. At present, liver- related studies are mainly conducted using in vivo models and cell lines, making it difficult to generalize the full picture of the structural characteristics and functions of human organs. Organoid is a three-dimensional (3D) culture system in vitro, which holds the promise to establish various disease models and conduct in-depth research by generating organ-like tissues in a dish. Recent advances in human liver organoids have provided a deeper understanding of this complex organ. In this review, we provide a systematic overview of the construction methods of organoids, focusing on their applications in the hepatic organogenesis and various liver disease models, as well as the limitations of current models. The development of organoid models is proving to be crucial in future liver research.
Bladder cancer (BC) is a common genitourinary malignancy worldwide. However, the molecular pathogenesis of BC remains unclear. The current study conducted bioinformatic analyses to discover key genes involved in BC progression. A total of 375 differentially expressed genes (DEGs) were screened in the GEO database and The Cancer Genome Atlas (TCGA) database, which were further evaluated by the core level in the protein–protein interaction network. RAC3 (Rac family small GTPase 3), one of the top hub genes, was focused on for its gene expression and prognostic value in BC. Immunohistochemical assays indicated elevated RAC3 levels in BC tissues compared with normal tissues. Overexpression of RAC3 expression was closely associated with poor differentiation (p = 0.035), advanced TNM stage (p = 0.014), lymph metastasis (p = 0.033), and recurrence (p < 0.001). Kaplan–Meier and Cox proportional hazards analyses demonstrated that high RAC3 expression indicated poor survival of BC patients, which could serve as an independent prognostic factor for overall survival (HR = 3.159, p = 0.023) and disease-free survival (HR = 4.633, p = 0.002). Moreover, bioinformatic analyses indicated that RAC3 might be correlated with malignant phenotypes and immune infiltration of BC. Taken together, RAC3 could be a novel prognostic biomarker for BC.
Abstract Though treatment with immune checkpoint blockage (ICB) has greatly improved clinical outcomes, not all patients respond to this treatment. Being able to predict which patients are likely to respond would be a significant clinical advance. Thus there remains a need for predictive biomarkers to determine in advance those with the most potential to benefit from immune checkpoint blockade. To date, most biomarkers of response are identified in tumor tissue, whereas biomarkers that can be assessed from peripheral blood are more desirable, due to the ease of access and reproducibility of sampling. To identify biomarkers associated with ICB response from peripheral blood, we apply single cell RNA sequencing to 24 peripheral blood mononuclear cell (PBMC) samples, collected from 12 stage III and IV melanoma patients before and after treatment with anti-PD1 monotherapy. Among these 12 patients, 6 are responders and 6 are non-responders. After quality control, a total of 62,273 single cells remains for further analysis. To define the cell type landscape in an unbiased manner, unsupervised clustering is used. We identify 20 robust cell clusters, including two B cell clusters (B-cell_1; B-cell_2), three myeloid clusters (monocytes, myeloid-derived suppressor cells, and M2 macrophages), twelve clusters enriched for T/NK cells, two clusters of platelets, and Hematopoietic Stem and Progenitor Cells (HSPCs). Across all patients, cell composition differs between pre- and post-treatment samples. Strikingly, responders have significant greater proportions of B-cell_1 cells in their pre-treatment samples than non-responders. At gene level, our analysis identifies an individual overexpressed marker in B-cell_1, i.e. IGLC3, which is highly enriched in responders than non-responders before treatment. In summary, our analysis identifies specific cell type and gene within patients’ PBMC samples that can serve as predictive biomarkers for patient’s response to ICB. Citation Format: Qianqian Song, Elizabeth Forbes, Lance D. Miller, Pierre L. Triozzi, Liang Liu, Wei Zhang, David R. Soto-Pantoja. Single-cell liquid biopsy reveals circulating heterogeneity and converging subpopulations in relation to immunotherapy response in melanoma [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-127.
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