Tianhao Zhang

The successful launch of the first spaceborne CO2 IPDA LIDAR onboard Daqi-1 (DQ-1) in April 2022 marks a milestone in advancing scientific research. However, a notable discrepancy in the physical definitions of X _{co 2} products between the IPDA LIDAR and TCCON presents a challenge for directly using TCCON for verifying and evaluating the performance of DQ-1's X _{co 2} products. To address this, we propose a method based on statistical hypothesis testing to globally validate the X _{co 2} products derived from the spaceborne LIDAR. Our validation method does not compare DQ-1's observations with the TCCON observations. We only utilize the useful information from TCCON to simulate the probability distribution of the true X _{co 2} value in the LIDAR definition, and then the hypothesis testing is adopted for deriving the systematic error. Our method improves the accuracy of computing the systematic error of DQ-1 by over 50% compared to the traditional approach. Up to now, we have produced the first four months (2022.6∼2022.9) of DQ-1's X _{co 2} products. In this context, we present preliminary validation results based on the four months of data. Based on our method, we have validated that the accuracy of DQ-1's X _{co 2} products is 0.1±1 ppm. This approach not only sets the stage for future official global validations of DQ-1's X _{co 2} products but also holds promise for application in upcoming similar missions such as MERLIN and DQ-2.

Background: Depression is a prevalent psychiatric disorder with high long‐term morbidities, recurrences, and mortalities. Despite extensive research efforts spanning decades, the cellular and molecular mechanisms of depression remain largely unknown. What's more, about one third of patients do not have effective anti‐depressant therapies, so there is an urgent need to uncover more mechanisms to guide the development of novel therapeutic strategies. Adenosine triphosphate (ATP) plays an important role in maintaining ion gradients essential for neuronal activities, as well as in the transport and release of neurotransmitters. Additionally, ATP could also participate in signaling pathways following the activation of postsynaptic receptors. By searching the website PubMed for articles about “ATP and depression” especially focusing on the role of extracellular ATP (eATP) in depression in the last 5 years, we found that numerous studies have implied that the insufficient ATP release from astrocytes could lead to depression and exogenous supply of eATP or endogenously stimulating the release of ATP from astrocytes could alleviate depression, highlighting the potential therapeutic role of eATP in alleviating depression. Aim: Currently, there are few reviews discussing the relationship between eATP and depression. Therefore, the aim of our review is to conclude the role of eATP in depression, especially focusing on the evidence and mechanisms of eATP in alleviating depression. Conclusion: We will provide insights into the prospects of leveraging eATP as a novel avenue for the treatment of depression.

High temporal resolution aerosol optical depth (AOD) observations derived from new-generation geostationary (GEO) satellite possess unique advantages in analyzing aerosol fast variation processes and thereby providing more accurate assessments on their climate effects and health risks. Unfortunately, the expected advantages and values are dramatically limited by relatively large proportion of data missing in the GEO AOD products due to cloud obscuration and intrinsic retrieval algorithm. Although several data recovery algorithms have been proposed in recent years to improve the spatial coverage for GEO AOD products, yet most of them aims at filling up the data blanks rather than reconstructing the temporally continuous variation of aerosol. Accordingly, in this study, a novel framework of nested spatiotemporal fusion blending GEO with sun-synchronous orbit (SSO) satellite observations based on Bayesian maximum entropy (BME) theorem is developed for GEO Advanced Himawari-8 Imager (AHI) AOD recovery with the sufficient excavation of complementary information from GEO and SSO satellite observations, where the minute-stage and hour-stage BME fusion are jointly employed to reconcile temporal inconsistency and data discrepancies between GEO and SSO observations. The results demonstrate that the AOD spatial coverage is dramatically increased by 240.9% (from 20.5% to 70%) with ensured accuracy after Nested-BME fusion. Additionally, two case analyses, during the development and dispersion processes of haze respectively, both demonstrate that the proposed Nested-BME fusion framework could reconstruct the reliable aerosol diurnal variation trends on the basis of recovering missing data for Himawari-8 AHI AOD datasets, while the AHI official level-2 and level-3 AOD products fail to capture these key trends. Furthermore, the developed Nested-BME AOD fusion framework is also applicable for other geostationary satellites over other regions, which could substantially enhance the availability and value of high temporal resolution AOD products for better scientific applications.

High-frequency aerosol observation from a new-generation geostationary meteorological satellite is capable to capture and monitor the spatiotemporal dynamic variation of aerosols, which is of vital significance to environmental research and climate studies. Due to the diversity and complexity of land cover, it is a challenge to retrieve aerosol properties with high accuracy over land, especially over heterogeneous land surfaces. In this study, a geometry-discrete minimum reflectance aerosol retrieval algorithm (GeoMRA) has been proposed to retrieve 10-min high temporal resolution aerosol optical depth (AOD) datasets for geostationary Himawari-8 Advanced Himawari Imager (AHI) sensor, aiming at providing universal bidirectional reflectance distribution function (BRDF) descriptions for different land surfaces with different heterogeneous extent. The AOD retrievals from GeoMRA demonstrate good consistency against the ground-based AERONET measurements in East Asia from 2015 to 2020, with a correlation coefficient ( ${R}$ ) of 0.883 and approximately 65.6% of matchups falling within the expected error envelope of ±(0.05% +15%). Intercomparison between the GeoMRA retrieved AOD and other operational AOD products shows that the GeoMRA AOD retrievals, which generally possess similar spatial distribution and accuracy as Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products, have better performances than the Japan Aerospace Exploration Agency (JAXA) AOD products by providing more accurate AOD retrievals with higher spatial coverage. Moreover, the AOD bias analyses further demonstrate the robustness of GeoMRA algorithm, and an extreme haze event shows that the continuous GeoMRA AOD images illustrate smoother temporal variations than JAXA AOD products, demonstrating its efficacy and reliability in capturing the process of haze transport and monitoring the continuous spatiotemporal variation of aerosol. The above results suggest the considerable accuracy of GeoMRA algorithm for scientific application requirement and demonstrate the robustness of the proposed BRDF scheme in describing heterogeneous surfaces with diverse reflectance distribution.

Decresased responses to immune checkpoint inhibitors (ICIs) in NSCLC are associated with low tumor mutational burden (TMB), impaired tumor antigen presentation, decreased baseline CD8⁺ T lymphocytes tumor infiltration and low PD-L1 expression in the tumor microenvironment (TME). One approach to augment the efficacy of ICIs in NSCLC is to enhance anti-tumor T cell responses by utilizing in situ vaccination (ISV) with professional antigen-presenting dendritic cells (DCs). In preclinical studies as well as a phase I clinical trial, we have shown that ISV with gene-modified DCs expressing CCL21 (CCL21-DC) promotes effector T lymphocyte infiltration into the tumor, PD-L1 upregulation, and systemic tumor-specific immune responses. We hypothesized that in situ vaccination with CCL21-DC could promote T cell priming and activation, thereby sensitizing non-responsive NSCLC tumors to ICI. Utilizing multiple syngeneic murine models of NSCLC with distinct driver mutations [Kras^G12D/P53^+/-/Lkb1^-/- (KPL); Kras^G12D/P53^+/- (KP); and Kras^G12D (K)] and varying TMB, we observed that CCL21-DC ISV synergizes with anti-PD-1 therapy resulting in the eradication of a subset of tumors. Evaluation of KPL tumors by flow cytometry and scRNA-seq revealed that combination therapy reprogrammed the myeloid compartment to be less immunosuppressive, and induced T cell infiltration, Th1 polarization, as well as the enrichment of stem-like TCF1+ T cells within the TME. Intratumoral (IT) CCL21-DC enhanced CD103⁺DC migration to the local lymph node as determined by flow cytometry. Addition of ICI to IT CCL21-DC resulted in enhanced T cell proliferation and INF-γ secretion within the TME and the spleen. Whole exome sequencing (WES) of tumors revealed immunoediting of tumor subclones after IT CCL21-DC and ICI combination therapy. Tumor rechallenge studies demonstrated the establishment of systemic tumor-specific immunity in a subset of mice that were cured of disease.In conclusion, tumor vaccination with CCL21-DC sensitizes immune-resistant murine NSCLC to anti-PD-1 therapy. Combination therapy enhances anti-tumor T cell responses, resulting in immunoediting of tumor subclones and the generation of systemic tumor-specific immunity. In an ongoing clinical trial, we are evaluating the safety and efficacy of ISV with CCL21-DC in combination with anti-PD-1, pembrolizumab, in patients with advanced NSCLC who have progressed on front-line therapy. Citation Format: Ramin Salehi-Rad, Raymond J. Lim, Linh M. Tran, Yushen Du, Camelia Dumitras, Tianhao Zhang, William Crosson, Jensen Abascal, Kostyantyn Krysan, Bin Liu, Steven M. Dubinett. In situ vaccination with CCL21-DC sensitizes non-responsive murine NSCLCs to anti-PD-1 therapy and generates systemic tumor-specific immune memory [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4206.

Satellite-derived aerosol optical depth (AOD) is an important parameter for studies related to atmospheric environment, climate change, and biogeochemical cycle. Unfortunately, the relatively high data missing ratio of satellite-derived AOD limits the atmosphere-related research and applications to a certain extent. Accordingly, numerous AOD fusion algorithms have been proposed in recent years. However, most of these algorithms focused on merging AOD products from multiple passive sensors, which cannot complementarily recover the AOD missing values due to cloud obscuration and the misidentification between optically thin cloud and aerosols. In order to address these issues, a spatiotemporal AOD fusion framework combining active and passive remote sensing based on Bayesian maximum entropy methodology (AP-BME) is developed to provide satellite-derived AOD data sets with high spatial coverage and good accuracy in large scale. The results demonstrate that AP-BME fusion significantly improves the spatial coverage of AOD, from an averaged spatial completeness of 27.9%–92.8% in the study areas, in which the spatial coverage improves from 91.1% to 92.8% when introducing Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD data sets into the fusion process. Meanwhile, the accuracy of recovered AOD nearly maintains that of the original satellite AOD products, based on evaluation against ground-based Aerosol Robotic Network (AERONET) AOD. Moreover, the efficacy of the active sensor in AOD fusion is discussed through overall accuracy comparison and two case analyses, which shows that the provision of key aerosol information by the active sensor on haze condition or under thin cloud is important for not only restoring the real haze situations but also avoiding AOD overestimation caused by cloud optical depth (COD) contamination in AOD fusion results.

The immense problem of missing satellite aerosol retrievals (Aerosol Optical Depth, (AOD)) detrimentally affects the prediction ability of ground-level PM_2.5 concentrations and may lead to unavoidable biases. An appropriate missing-imputation method has not been well developed to date. This study developed a two-stage approach (AOD-imputation stage and PM_2.5-prediction stage) to predict short-term PM_2.5 exposure in mainland China from 2013–2018. At the AOD-imputation stage, geostatistical methods and machine learning (ML) algorithms were examined to interpolate 1 km satellite aerosol retrievals. At the PM_2.5-prediction stage, the daily levels of PM_2.5 were predicted at a resolution of 1 km, based on interpolated AOD and meteorological data. The statistical performances of the different interpolation methods were comprehensively compared at each stage. The original coverage of retrieved AOD was 15.46% on average. For the AOD-imputation stage, ML methods produced a higher coverage (98.64%) of AOD than geostatistical methods (21.43–87.31%). Among ML algorithms, random forest (RF) or extreme gradient boosted (XG-interpolated) AOD produced better interpolated quality (CV R² = 0.89 and 0.85) than other algorithms (0.49–0.78), but XGBoost required only 15% of the computing time of RF. For the PM_2.5 predicted stage, neither RF-AOD nor XG-AOD could guarantee higher accuracy in PM_2.5 estimations (CV R² = 0.88 (RF or XG-AOD) compared to 0.85 (original)), or more stable spatial and temporal extrapolation (spatial, (temporal) CV R² = 0.83 (0.83), 0.82 (0.82), and 0.65 (0.61) for RF, XG, and original). For the AOD-imputation stage, the missing-filled efficiency depended more on external information, while the missing-filled accuracy relied more on model structure. For the PM_2.5 predicted stage, efficient AOD interpolation (or the ability to eliminate the missing data) was a precondition for the stable spatial and temporal extrapolation, while the quality of interpolated AOD showed less significant improvements. It was found that XG-AOD is a better choice to estimate daily PM_2.5 exposure in health assessments.

Environmental changes induced by ongoing anthropogenic activities have caused severe lake degradation. Because of the lack of long-term records, few studies have investigated the change in Wuhan lakes, and the effect of human activities on regional lake changes prior to 1973 has not been systematically studied yet. Therefore, in this study, historical maps and Landsat images were combined to track these changes from the 1920s to 2015. Three phases could be identified over the nearly 100-year study period. The most dramatic lake reduction (−21.53 km² yr⁻¹) occurred during Phase II (1950s–1980s) rather than Phase III (after the 1980s), as indicated by previous studies; the decreased lake area in Phase II was almost double that in Phase III. This reduction could be attributed to major hydraulic engineering projects during Phase II based on the watershed-scale analysis. In addition, land-use conversion over the past 45 years was used to quantify the impact of human exploitation on lakes. The shrinkage of lakes was predominately driven by agricultural activities, such as reclamation (39.2%) and aquaculture development (29.0%), and urbanization was a secondary driving force (19.8%), despite the rapid economic development of Wuhan. This study therefore provides a practical guide for lake protection in other areas similar to Wuhan.

Tuberculosis (TB) has a very high mortality rate worldwide. However, only a few studies have examined the associations between short-term exposure to air pollution and TB incidence. Our objectives were to estimate associations between short-term exposure to air pollutants and TB incidence in Wuhan city, China, during the 2015–2016 period. We applied a generalized additive model to access the short-term association of air pollution with TB. Daily exposure to each air pollutant in Wuhan was determined using ordinary kriging. The air pollutants included in the analysis were particulate matter (PM) with an aerodynamic diameter less than or equal to 2.5 micrometers (PM_2.5), PM with an aerodynamic diameter less than or equal to 10 micrometers (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ground-level ozone (O₃). Daily incident cases of TB were obtained from the Hubei Provincial Center for Disease Control and Prevention (Hubei CDC). Both single- and multiple-pollutant models were used to examine the associations between air pollution and TB. Seasonal variation was assessed by splitting the all-year data into warm (May–October) and cold (November–April) seasons. In the single-pollutant model, for a 10 μg/m³ increase in PM_2.5, PM₁₀, and O₃ at lag 7, the associated TB risk increased by 17.03% (95% CI: 6.39, 28.74), 11.08% (95% CI: 6.39, 28.74), and 16.15% (95% CI: 1.88, 32.42), respectively. In the multi-pollutant model, the effect of PM_2.5 on TB remained statistically significant, while the effects of other pollutants were attenuated. The seasonal analysis showed that there was not much difference regarding the impact of air pollution on TB between the warm season and the cold season. Our study reveals that the mechanism linking air pollution and TB is still complex. Further research is warranted to explore the interaction of air pollution and TB.