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Based on the mixed ligands of 1,4‐bis(2,4,6‐tricarboxylpyrid‐5‐yl)benzene (H6BTPB) and 1,4‐bis(imidazol‐1‐yl)benzene (bib), a new cadmium(II) coordination polymer with the molecular formula of {[Cd(bib)(μ2‐H2O)(H2O)2](H4BTPB)0.5(bib)0.5}n (CdCP) was constructed under solvothermal condition, and displayed a 3D supramolecular structure expanded by the hydrogen bonds between the 1D [Cd(bib)(μ2‐H2O)(H2O)2]n ladder chain and the guests. Profiting from its superior stability and inherent luminescence, the prepared CdCP dispalyed great potential as a highly sensitive and selective luminescent sensor in trace detection of 3‐NT biomarker in both water and real bodily fluids through the luminescence quenching effects, with the Ksv and LODs being 2.408×105 M−1 and 30.3 nM in water, 2.230×105 M−1 and 44.7 nM in diluted urine, 2.249×105 M−1 and 43.9 nM in diluted serum, respectively. Subsequent mechanism studies indicated the synergistic effect of internal filtering effect, photo‐induced electron transfer, and fluorescence resonance energy transfer determining the luminescence quenching response of CdCP towards 3‐NT biomarker. [ABSTRACT FROM AUTHOR]

Exosomes play significant roles in the communications between tumor cells and tumor microenvironment. However, the specific mechanisms by which exosomes modulate tumor development under hypoxia in pancreatic neuroendocrine tumors (pNETs) are not well understood. This study aims to investigate these mechanisms and made several important discoveries. We found that hypoxic exosomes derived from pNETs cells can activate tumor‐associated macrophages (TAM) to the M2 phenotype, in turn, the M2‐polarized TAM, facilitate the migration and invasion of pNETs cells. Further investigation revealed that CEACAM5, a protein highly expressed in hypoxic pNETs cells, is enriched in hypoxic pNETs cell‐derived exosomes. Hypoxic exosomal CEACAM5 was observed to induce M2 polarization of TAM through activation of the MAPK signaling pathway. Coculturing pNETs cells with TAM or treated with hypoxic exosomes enhanced the metastatic capacity of pNETs cells. In conclusion, these findings suggest that pNETs cells generate CEACAM5‐rich exosomes in a hypoxic microenvironment, which in turn polarize TAM promote malignant invasion of pNETs cells. Targeting exosomal CEACAM5 could potentially serve as a diagnostic and therapeutic strategy for pNETs. [ABSTRACT FROM AUTHOR]

The trace detection of biomarkers in real samples has profound a most important significance in the early diagnosis and daily monitoring of diseases. Based on the ligand pre‐design strategy, a novel 3D CuMOF, with the formula of {[Cu4(BTPB)(μ2‐OH)2(H2O)5]·3H2O}n, was fabricated by using the hexacarboxyl ligand of 1,4‐bis(2,4,6‐tricarboxylpyrid‐5‐yl)benzene (H6BTPB) and tetranuclear {Cu4(COO)4(μ2‐OH)2} SBUs. Benefiting from the robust framework and unique luminescence performance, the prepared CuMOF displays great potential as a dual‐responsive efficient luminescent sensor in "turn‐off" detection of 3‐nitrotyrosine (3‐NT) biomarker and "turn‐on" detecting the anthrax biomarker of dipicolinic acid (DPA), with detection limits (LOD) for 3‐NT and DPA being 110.8 ng/ml and 85.2 ng/ml, respectively. Additionally, the practicality and compatibility of such developed sensors were verified by quantifying 3‐NT and DPA biomarkers in diluted serum and urine samples with satisfactory recoveries. Further, the theoretical calculations of energy levels as well as the spectral overlap between the analytes and CuMOF were conducted to elucidate the possible sensing mechanisms. This work demonstrated that MOFs‐based luminescent sensors are evolving as an efficacious and equable approach for the detection of biomarkers in real samples. [ABSTRACT FROM AUTHOR]

Pancreatic neuroendocrine neoplasms (pNENs) are among the most frequently occurring neuroendocrine neoplasms (NENs) and require targeted therapy. High levels of fatty acid binding protein 5 (FABP5) are involved in tumor progression, but its role in pNENs remains unclear. We investigated the mRNA and protein levels of FABP5 in pNEN tissues and cell lines and found them to be upregulated. We evaluated changes in cell proliferation using CCK‐8, colony formation, and 5‐ethynyl‐2′‐deoxyuridine assays and examined the effects on cell migration and invasion using transwell assays. We found that knockdown of FABP5 suppressed the proliferation, migration, and invasion of pNEN cell lines, while overexpression of FABP5 had the opposite effect. Co‐immunoprecipitation experiments were performed to clarify the interaction between FABP5 and fatty acid synthase (FASN). We further showed that FABP5 regulates the expression of FASN via the ubiquitin proteasome pathway and both proteins facilitate the progression of pNENs. Our study demonstrated that FABP5 acts as an oncogene by promoting lipid droplet deposition and activating the WNT/β‐catenin signaling pathway. Moreover, the carcinogenic effects of FABP5 can be reversed by orlistat, providing a novel therapeutic intervention option. [ABSTRACT FROM AUTHOR]

Although most patients with acute viral myocarditis recover spontaneously, some patients progress to heart failure. Perturbations in innate immunity may partially explain the heterogeneity of clinical outcomes. As the most abundant immune cells in the heart, cardiac macrophages have heterogeneous origins, including embryonic‐derived resident macrophages (ResMϕs) and monocyte‐derived macrophages (MoMFs). However, the time course change and role of cardiac macrophage subsets has not been fully explored. In the present study, we found that BALB/c mice had prolonged MoMF accumulation and low proportions of ResMϕs that could not be restored to normal levels. MoMFs of BALB/c mice generally exhibit an M1‐dominant functional phenotype. Moreover, the preferential depletion of MoMF by a C‐C chemokine receptor type 2 (CCR2) inhibitor resulted in improved acute myocarditis and chronic fibrosis, as well as the recovery of ResMϕs number and reduced CD4+ T cell expansion. Hence, immunomodulatory therapy that targets the balance among cardiac macrophages and modulates their function is expected to prevent the progression of cardiac injury to overt heart failure and improve adverse outcomes. [ABSTRACT FROM AUTHOR]

Quantifying the response of atmospheric rivers (ARs) to radiative forcing is challenging due to uncertainties caused by internal climate variability, differences in shared socioeconomic pathways (SSPs), and methods used in AR detection algorithms. In addition, the requirement of medium‐to‐high model resolution and ensemble sizes to explicitly simulate ARs and their statistics can be computationally expensive. In this study, we leverage the unique 50‐km large ensembles generated by a Geophysical Fluid Dynamics Laboratory next‐generation global climate model, Seamless system for Prediction and EArth system Research, to explore the warming response in ARs. Under both moderate and high emissions scenarios, increases in AR‐day frequency emerge from the noise of internal variability by 2060. This signal is robust across different SSPs and time‐independent detection criteria. We further examine an alternative approach proposed by Thompson et al. (2015), showing that unforced AR variability can be approximated by a first‐order autoregressive process. The confidence intervals of the projected response can be analytically derived with a single ensemble member. Plain Language Summary: An "Atmospheric River" (AR) is a weather phenomenon characterized by strong, narrow moisture transport that brings heavy rainfall to land. They serve as a critical water resource but also can cause damaging flash floods and high winds. Thus, knowing how AR activity will change in the future climate can help us to mitigate potential AR‐related disasters and promote effective water resource management. However, this task is challenging due to uncertainty in how fast the climate will warm and in how much the noise of natural climate variability can obscure the signal from global warming. In addition, several different definitions of AR exist, which raises questions about the sensitivity of AR changes to AR definition. In this study, we use a next‐generation global climate model to evaluate the influence of these uncertainties and to determine the time when humanity will notice a discernible change in AR activity. We find that the response to global warming can be robustly identified by 2060 across all explored methods of computation. We further examine a less expensive approach, which enables us to quantify the uncertainty in warming signals and estimate the time of signal emergence in a single realization of nature. Key Points: Increases in atmospheric river (AR) frequency emerge from the noise on internal variability by 2060 in simulations with a global climate modelA computationally efficient method is developed for determining the time of emergence (ToE) of AR responses to global warmingThe AR ToE is not sensitive to the criteria of AR detection algorithm or shared Socioeconomic pathways [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) exert significant socioeconomic impacts in western North America, where 30% of the annual precipitation is determined by ARs that occur in less than 15% of wintertime. ARs are thus beneficial to water supply but can produce extreme precipitation hazards when making landfall. While most prevailing research has focused on the subseasonal (≤5 weeks) prediction of ARs, only limited efforts have been made for AR forecasts on multiseasonal timescales (≥3 months) that are crucial for water resource management and disaster preparedness. Through the analysis of reanalysis data and retrospective predictions from a new seasonal‐to‐decadal forecast system, this research shows the existing potential of multiseasonal AR frequency forecasts with predictive skills 9 months in advance. Additional analysis explores the dominant predictability sources and challenges for multiseasonal AR prediction. Plain Language Summary: Atmospheric rivers (ARs), narrow corridors of intense moisture transport and heavy precipitation, are an important water resource but also a cause of flooding‐related disasters for western North America. Consequently, predictions of AR frequency several seasons in advance potentially would be of great value, but such operational forecasts are currently lacking due to the challenges in simulating such intense, small‐scale weather phenomena and their predictability sources on seasonal timescales. In this study, we examine the forecast skill of AR frequency on seasonal‐to‐multiseasonal timescales (≥3 months) in a new generation seasonal‐to‐decadal prediction system developed at the Geophysical Fluid Dynamics Laboratory. We find that AR frequency can be skillfully forecast at least 9 months in advance over certain regions of the west coast of North America, such as California and Alaska, while the forecasts are only reliable for the first season in other regions. This regional variability can be further explained by the large‐scale climate variability pattern that is responsible for much of the skill, which is strongly modulated by slowly varying sea surface temperature (SST) variations. A prototype probabilistic seasonal AR forecast product is proposed. Key Points: Atmospheric river frequency can be skillfully forecast at least 9 months in advanceThe skills are significant over certain regions of western North America such as Alaska and CaliforniaEl Nino‐Southern Oscillation and Interdecadal Pacific Oscillation are important predictability sources [ABSTRACT FROM AUTHOR]

The next‐generation seasonal prediction system is built as part of the Seamless System for Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). SPEAR is an effort to develop a seamless system for prediction and research across time scales. The ensemble‐based ocean data assimilation (ODA) system is updated for Modular Ocean Model Version 6 (MOM6), the ocean component of SPEAR. Ocean initial conditions for seasonal predictions, as well as an ocean state estimation, are produced by the MOM6 ODA system in coupled SPEAR models. Initial conditions of the atmosphere, land, and sea ice components for seasonal predictions are constructed through additional nudging experiments in the same coupled SPEAR models. A bias correction scheme called ocean tendency adjustment (OTA) is applied to coupled model seasonal predictions to reduce model drift. OTA applies the climatological temperature and salinity increments obtained from ODA as three‐dimensional tendency terms to the MOM6 ocean component of the coupled SPEAR models. Based on preliminary retrospective seasonal forecasts, we demonstrate that OTA reduces model drift—especially sea surface temperature (SST) forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño–Southern Oscillation (ENSO). Plain Language Summary: Dynamic seasonal prediction systems employ global climate models to predict climate variations on monthly to seasonal time scales. A new state‐of‐the‐art seasonal prediction system has been developed at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). The prediction models are based on GFDL's new component models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The new seasonal prediction system includes new ways to apply observational information to initialize the model simulations, including an updated data assimilation system for the Modular Ocean Model Version 6 (MOM6). Bias correction is applied to the coupled dynamic models to reduce prediction bias, namely, the gap between model‐simulated and real‐world climatology. Preliminary seasonal prediction experiments demonstrate reduced errors in the predicted climate state globally, as well as improved prediction skill for applications such as El Niño–Southern Oscillation (ENSO). Key Points: The next‐generation SPEAR seasonal prediction system uses the recently developed coupled general circulation model at GFDLThe updated ocean data assimilation system for the MOM6 ocean model produces ocean state estimation and initial conditions for predictionsThe new coupled model and ocean tendency adjustment (OTA) reduce model forecast drift and improve seasonal prediction skill [ABSTRACT FROM AUTHOR]

There are no licensed drugs for nonalcoholic fatty liver disease (NAFLD), and there is a lack of consensus on the best outcome measures for controlled trials. This systematic review aimed to evaluate the efficacy of GLP‐1 RAs in the management of NAFLD, the degree of heterogeneity in trial design and the robustness of conclusions drawn from these clinical trials. We searched publication databases and clinical trial registries through 2 November 2019 for clinical trials with NAFLD. We evaluated improvements in histological findings, noninvasive markers of hepatic steatosis, inflammation, and fibrosis, insulin resistance and anthropometric measures. Our final analysis included 24 clinical trials, comprising 6313 participants with a mean duration of 37 weeks. Four clinical trials, including RCT (n = 1), single‐arm studies (n = 2) and case series studies (n = 1), used biopsy‐confirmed liver histological change as their end‐points. The remaining studies (n = 20) used surrogate end‐points. GLP‐1 RAs were effective for the improvement in hepatic inflammation, hepatic steatosis and fibrosis. More importantly, GLP‐1 RAs showed promise in improving the histological features of NASH. In addition, 8 ongoing trials were identified. In this systematic review of published and ongoing clinical trials of the efficacy of GLP‐1RAs for NAFLD, we found that GLP‐1 RAs are effective for hepatic steatosis and inflammation, with the potential to reverse fibrosis. Further prospective studies of sufficient duration using histological end‐points are needed to fully assess the efficacy of GLP‐1 RAs in the management of NAFLD. [ABSTRACT FROM AUTHOR]

We document the development and simulation characteristics of the next generation modeling system for seasonal to decadal prediction and projection at the Geophysical Fluid Dynamics Laboratory (GFDL). SPEAR (Seamless System for Prediction and EArth System Research) is built from component models recently developed at GFDL—the AM4 atmosphere model, MOM6 ocean code, LM4 land model, and SIS2 sea ice model. The SPEAR models are specifically designed with attributes needed for a prediction model for seasonal to decadal time scales, including the ability to run large ensembles of simulations with available computational resources. For computational speed SPEAR uses a coarse ocean resolution of approximately 1.0° (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1° to 0.25°. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM4 models but with design choices geared toward seasonal to multidecadal physical climate prediction and projection. We document simulation characteristics for the time mean climate, aspects of internal variability, and the response to both idealized and realistic radiative forcing change. We describe in greater detail one focus of the model development process that was motivated by the importance of the Southern Ocean to the global climate system. We present sensitivity tests that document the influence of the Antarctic surface heat budget on Southern Ocean ventilation and deep global ocean circulation. These findings were also useful in the development processes for the GFDL CM4 and ESM4 models. Plain Language Summary: In this paper we describe the development and simulation characteristics of a new climate model that will be used for seasonal to multidecadal climate prediction and projection. The model combines a set of newly developed components that simulate the ocean, atmosphere, land, and sea ice. We document this model by assessing its performance in simulating the current climate and by showing the model's response to changing greenhouse gases and aerosols over the 20th and 21st centuries. We also show results from a set of sensitivity experiments that were an important part of the model development process. These sensitivity tests explore connections between the surface energy balance over Antarctica and the circulation of the deep ocean. Key Points: Development and performance of the next generation GFDL seasonal to decadal prediction model is documentedThe response of this model to realistic radiative forcing changes is shown via a large ensemble of climate simulations for 1921–2100The influence of the Antarctic surface energy balance on the world ocean was crucial in model development as shown via sensitivity tests [ABSTRACT FROM AUTHOR]

Abstract: The particle filter (PF) and the ensemble Kalman filter (EnKF) are two promising and popularly adopted types of ensemble‐based data assimilation methods for paleoclimate reconstruction. However, no systematic comparison between them has been attempted. We compare these two uncertainty based methods in pseudoproxy experiments where synthetic seasonal mean sea surface temperature observations are assimilated. Their skills are evaluated with regards to local, hemispherically averaged and globally averaged analysis error, and their ability to capture large‐scale modes of variability. It is found that the EAKF (Ensemble Adjustment Kalman filter, a variant of EnKF) performs better than the PF with only one third of the ensemble size, despite PF's theoretical superiority in allowing for non‐Gaussian statistics and nonlinear dynamics. The success of the EAKF is attributed to the facts that (1) Gaussian assumption is somewhat appropriate for this application; (2) The EAKF is less sensitive to sampling errors than the PF due to the different methodological natures. Sixteen members are enough to estimate accurate covariance for the EAKF, but 48 (even 96) members still underrepresent the state space of high‐dimensional system for the PF. Our study highlights the importance of a large localization radius in the application of the EnKF to paleoclimate reconstruction due to the sparse proxy network and suggests that additional techniques, such as localization or clustered particle filter, are needed to improve the PF for paleoclimate reconstruction, in addition to the simple importance resampling currently adopted by most research. [ABSTRACT FROM AUTHOR]