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Abstract Objectives The endoscopic bilateral stent‐in‐stent (SIS) deployment is a challenging procedure. Such difficulty is mainly caused by sticking of the tip of the delivery sheath into the self‐expandable metal stents (SEMSs) mesh, requiring an additional dilating procedure. Herein, we assessed the clinical results of using cross‐wired metal stent for endoscopic bilateral SIS deployment (BONASTENT M‐Hilar) in patients with malignant hilar biliary obstruction (MHBO) in both high‐volume and non‐high‐volume centers. Methods We prospectively enrolled consecutive patients with MHBO between February 2016 and December 2018 at eight centers. Results Forty‐six patients were enrolled during the study period. The proportions of technical success were 93.5% (43/46) and clinical success (CS) on intention‐to‐treat and per‐protocol analyses were 91.3% (42/46) and 93.0% (40/43), respectively. The proportion of an additional dilating procedure during the primary procedure was 50.0% (23/46). Recurrent biliary obstruction (RBO) on intention‐to‐treat analysis occurred in 32.6% (15/46) of cases. Almost all of the events were caused by stent ingrowth (14/15). The median survival time and time to RBO were 255 and 349 days, respectively. The probability of stent patency at 3, 6, and 12 months was 86.5%, 63.9%, and 47.6%, respectively. Conclusions The cross‐wired metal stent had excellent technical and CS, although non‐high‐volume centers were included in this study (UMIN000021441).

Warming and precipitation variations have significant impacts on carbon uptake in the Tibetan Plateau. However, which climatic variable or process primarily drives the inter-annual variations of carbon uptake is not clear. Using multiple gross primary productivity (GPP) estimates, we study the controlling factors of the previous-year lagged effect and evaluate the effects of climate variables on GPP in the Tibetan Plateau. Results show that the lagged ecosystems in which productivity is significantly associated with previous-year precipitation are more sensitive to biotic and environmental factors than unlagged ones. In addition, previous-year precipitation as a whole has a positive impact on GPP over the Tibetan Plateau. Furthermore, precipitation regimes including precipitation intensity and dry-days fraction, and current-year precipitation amount primarily drive the GPP variabilities instead of temperature, vapor pressure deficit (VPD), and radiation at the grid scale. Future projections suggest that precipitation amount and intensity will increase, and dry-days fraction will decrease, which indicates that precipitation might have a more complicated impact on carbon uptake via variations in intra-distribution. Our study reveals the primary climatic factors that influence the variations of carbon uptake in the Tibetan Plateau, offering valuable insights for accurate carbon cycle modeling.

Abstract Background Early prediction of treatment response is crucial for the optimal treatment of advanced breast cancer. We aimed to explore whether monitoring early changes in plasma human epidermal growth factor receptor 2 (HER2) levels using digital PCR (dPCR) could predict the treatment response in advanced breast cancer. Methods This was a multicenter, prospective, noninterventional clinical study of patients with advanced breast cancer. All enrolled patients underwent blood testing to measure the HER2 levels by digital PCR before treatment initiation and once every 3 weeks during the study. The primary endpoints werea the diagnostic value of dPCR for detecting HER2 status in the blood andb the relevance of potential changes in the plasma HER2 level at 3 weeks from baseline for predicting treatment response. Results Overall, 85 patients were enrolled between October 9, 2018, and January 23, 2020. dPCR had a specificity of 91.67% (95% CI: 80.61% to 97.43%) for detecting HER2 amplification, and the area under the receiver operating characteristic (ROC) curve was 0.84 (p

Using satellite observations over the recent two decades, the authors quantify the biophysical impact of earth greening on land surface temperature and show a considerable cooling effect in India and China, important for climate mitigation.

Accurate quantification of turbulent heat fluxes [THF, comprising sensible heat flux (SHF) and latent heat flux (LHF)] and Bowen ratio ( β , ratio of SHF and LHF) is essential for understanding the air–sea interaction. However, the biased estimates of SHF and LHF by widely applied bulk aerodynamic models, and the separate estimates of SHF and LHF by data-driven models, both may result in unrealistic β estimates. This study for the first time innovatively proposes a Bowen ratio-informed data-driven model (BrTHF) for coordinating the estimations of THF and β using the multivariate random forest (RF) technique and a combination of eddy covariance flux observations and meteorological and oceanic observations collected from 53 historical cruises. The result shows that the BrTHF model could not only achieve high-accuracy SHF and LHF estimates, but also avoid the outliers of β estimates that were commonly found in un-synergistic RF and well-known Coupled Ocean Atmosphere Response Experiment 3.5 models.

Accurate quantification of terrestrial gross primary production (GPP) is integral for enhancing our understanding of the global carbon budget and climate change. The light use efficiency (LUE) model is undoubtedly the most extensively applied method for GPP estimation. However, the two-leaf (TL)-LUE model using a ‘potential’ sunlit leaf area index (LAI _su ) can separate a portion of LAI _su even when the canopy does not receive any direct radiation, leading to the underestimation of GPP under cloudy and overcast days. Here, we developed a dynamic-leaf (DL) LUE model by introducing an ‘effective’ LAI _su to improve GPP estimation, which considers the comprehensive contribution of LAI _su when the canopy does and does not receive direct radiation. In particular, the new model decreases LAI _su to zero when direct radiation reaches zero. Our evaluation at eight ChinaFLUX sites showed that (1) the DL-LUE model outperformed the most well-known BL-LUE (namely, the MOD17 GPP algorithm) and TL-LUE models in reproducing the daily in situ GPP, especially at four forest sites [reducing the root mean square error (RMSE) from 1.74 g C m ^−2 d ^−1 and 1.53 g C m ^−2 d ^−1 to 1.36 g C m ^−2 d ^−1 and increasing the coefficient of determination ( R ^2 ) from 0.74 and 0.79–0.82, respectively]. Moreover, the improvements were particularly pronounced at longer temporal scales, as indicated by the RMSE decreasing from 29.32 g C m ^−2 month ^−1 and28.11 g C m ^−2 month ^−1 to 25.81 g C m ^−2 month ^−1 at a monthly scale and from 231.82 g C m ^−2 yr ^−1 and 221.60 g C m ^−2 yr ^−1 –200.00 g C m ^−2 yr ^−1 at a yearly scale; (2) the DL-LUE model mitigated the systematic underestimation of the in situ GPP by both the TL-LUE and BL-LUE models when the clearness index (CI) was below 0.5, as indicated by the Bias reductions of 0.25 g C m ^−2 d ^−1 and 0.46 g C m ^−2 d ^−1 , respectively; and (3) the contributions of the shaded GPP to the total GPP from the DL-LUE model were higher by 0.07–0.16 than those from the TL-LUE model across the eight ChinaFLUX sites. The proposed parsimonious and effective DL-LUE model not only has great potential for improving global GPP estimations but also provides a more mechanism-based approach for partitioning the total GPP into its shaded and sunlit components.

ObjectivesThis study aimed to discuss the clinical characteristics and emergent endovascular treatment of carotid cavernous fistulas presenting as intracranial hemorrhage.MethodsThe clinical data of five patients with carotid cavernous fistulas, who presented with intracranial hemorrhage and who were admitted from January 2010 to April 2017, were analyzed retrospectively, and the diagnoses were confirmed by head computed tomography. Digital subtraction angiography was carried out in all the patients for the diagnosis and further emergent endovascular procedures. All patients were followed up to assess the clinical outcomes.ResultsIn total, five patients harbored five mono-lateral lesions; two of them were obliterated by detachable balloons, two by detachable coils, and one by detachable coils and Onyx glue. Only one patient was cured by another detachable balloon in the second session, and the other four patients were cured in the first session. At the 3- to 10-year follow-up, there was no intracranial re-hemorrhage in any of the patients; there was no recurrence of symptoms; and delayed occlusion of the parent artery was noted in one case.ConclusionEmergent endovascular therapy is indicated for carotid cavernous fistulas presenting as intracranial hemorrhage. Individualized treatment according to the characteristics of different lesions is safe and effective.

The Text-to-SQL task in the field of semantic parsing is of great significance for realizing database-based automatic question and answer.At present, deep learning models, such as sequence generation model Seq2Seq, has achieved significant effects in single-table SQL queries.However, the problem of multi-table SQL queries remains to be solved.Graph neural network can effectively extract the associated information between databases, tables and questions, enrich the semantic information in the parsing process, and improve the accuracy of multi-table SQL queries.This paper proposes an adaptive graph construction method and graph encoding method.Question information is introduced into the existing Text-to-SQL model, and the graph network initialized weights are generated by convolution operation on the splicing word vector of the question sentence and the database.General training can be achieved for different databases of the same type.The IRNet framework and relational expansion are used to design the overall model, and it is verified on the open Text-to-SQL data set——Spider.Results show that the technology can effectively improve the matching accuracy of multi-table SQL statement generation, and the algorithm has an important reference value for the research of graph neural network in the text-to-SQL field.

Since 1982, Landsat series of satellite sensors continuously acquired thermal infrared images of the Earth’s land surface. In this study, Landsat 5, 7, and 8 land surface temperature (LST) products in the conterminous United States from 2009 to 2019 were validated using in situ measurements collected at 6 SURFRAD (Surface Radiation Budget Network) sites, 6 ARM (Atmospheric Radiation Measurement) sites, and 9 NDBC (National Data Buoy Center) sites. The results indicate that a relatively consistent performance among Landsat 5, 7, and 8 LST products is obtained for most sites due to the consistent LST retrieval algorithm in conjunction with the same atmospheric compensation and land surface emissivity (LSE) correction methods for Landsat 5, 7, and 8 sensors. Large bias and root mean square error (RMSE) of Landsat LST product are obtained at some vegetated sites due to incorrect LSE estimation where LSE is invariant with the increasing of normalized difference vegetation index (NDVI). Except for the sites with incorrect LSE estimation, a mean bias (RMSE) of the differences between Landsat LST and in situ LST is 1.0 K (2.1 K) over snow-free land surfaces, −1.1 K (1.6 K) over snow surfaces, and −0.3 K (1.1 K) over water surfaces.