Improving Pneumonia Classification and Lesion Detection Using Spatial Attention Superposition and Multilayer Feature Fusion

Pneumonia is a severe inflammation of the lung that could cause serious complications. Chest X-rays (CXRs) are commonly used to make a diagnosis of pneumonia. In this paper, we propose a deep-learning-based method with spatial attention superposition (SAS) and multilayer feature fusion (MFF) to facilitate pneumonia diagnosis based on CXRs. Specifically, an SAS module, which takes advantage of the channel and spatial attention mechanisms, was designed to identify intrinsic imaging features of pneumonia-related lesions and their locations, and an MFF module was designed to harmonize disparate features from different channels and emphasize important information. These two modules were concatenated to extract critical image features serving as the basis for pneumonia diagnosis. We further embedded the proposed modules into a baseline neural network and developed a model called SAS-MFF-YOLO to diagnose pneumonia. To validate the effectiveness of our model, extensive experiments were conducted on two CXR datasets provided by the Radiological Society of North America (RSNA) and the AI Research Institute. SAS-MFF-YOLO achieved a precision of 88.1%, a recall of 98.2% for pneumonia classification and an AP50 of 99% for lesion detection on the AI Research Institute dataset. The visualization of intermediate feature maps showed that our method could facilitate uncovering pneumonia-related lesions in CXRs. Our results demonstrated that our approach could be used to enhance the performance of the overall pneumonia detection on CXR imaging

ESSM: An Extractive Summarization Model with Enhanced Spatial-Temporal Information and Span Mask Encoding

Extractive reading comprehension is to extract consecutive subsequences from a given article to answer the given question. Previous work often adopted Byte Pair Encoding (BPE) that could cause semantically correlated words to be separated. Also, previous features extraction strategy cannot effectively capture the global semantic information. In this paper, an extractive summarization model is proposed with enhanced spatial-temporal information and span mask encoding (ESSM) to promote global semantic information. ESSM utilizes Embedding Layer to reduce semantic segmentation of correlated words, and adopts TemporalConvNet Layer to relief the loss of feature information. The model can also deal with unanswerable questions. To verify the effectiveness of the model, experiments on datasets SQuAD1.1 and SQuAD2.0 are conducted. Our model achieved an EM of 86.31% and a F1 score of 92.49% on SQuAD1.1 and the numbers are 80.54% and 83.27% for SQuAD2.0. It was proved that the model is effective for extractive QA task

Successional change in species composition alters climate sensitivity of grassland productivity.

Succession theory predicts altered sensitivity of ecosystem functions to disturbance (i.e., climate change) due to the temporal shift in plant community composition. However, empirical evidence in global change experiments is lacking to support this prediction. Here, we present findings from an 8-year long-term global change experiment with warming and altered precipitation manipulation (double and halved amount). First, we observed a temporal shift in species composition over 8 years, resulting in a transition from an annual C3 -dominant plant community to a perennial C4 -dominant plant community. This successional transition was independent of any experimental treatments. During the successional transition, the response of aboveground net primary productivity (ANPP) to precipitation addition magnified from neutral to +45.3%, while the response to halved precipitation attenuated substantially from -17.6% to neutral. However, warming did not affect ANPP in either state. The findings further reveal that the time-dependent climate sensitivity may be regulated by successional change in species composition, highlighting the importance of vegetation dynamics in regulating the response of ecosystem productivity to precipitation change

Evaluating Alternative Ebullition Models for Predicting Peatland Methane Emission and Its Pathways via Data–Model Fusion

Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion, together with their different transport rates and vulnerability to oxidation, determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways are highly uncertain. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: (1) the ebullition bubble growth volume threshold approach (EBG) and (2) the modified ebullition concentration threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model–data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 % to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization

Evaluating Alternative Ebullition Models for Predicting Peatland Methane Emission and Its Pathways via Data–Model Fusion

Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion, together with their different transport rates and vulnerability to oxidation, determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways are highly uncertain. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: (1) the ebullition bubble growth volume threshold approach (EBG) and (2) the modified ebullition concentration threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model–data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 % to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization

Evaluating alternative ebullition models for predicting peatland methane emission and its pathways via data–model fusion

Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion, together with their different transport rates and vulnerability to oxidation, determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways are highly uncertain. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: (1) the ebullition bubble growth volume threshold approach (EBG) and (2) the modified ebullition concentration threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model–data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 % to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization

Observation of high-temperature superconductivity in the high-pressure tetragonal phase of La2PrNi2O7-{\delta}

The recent discovery of high-temperature superconductivity in the Ruddlesden-Popper phase La3Ni2O7 under high pressure marks a significant breakthrough in the field of 3d transition-metal oxide superconductors. For an emerging novel class of high-Tc superconductors, it is crucial to find more analogous superconducting materials with a dedicated effort toward broadening the scope of nickelate superconductors. Here, we report on the observation of high-Tc superconductivity in the high-pressure tetragonal I4/mmm phase of La2PrNi2O7 above ~10 GPa, which is distinct from the reported orthorhombic Fmmm phase of La3Ni2O7 above 14 GPa. For La2PrNi2O7, the onset and the zero-resistance temperatures of superconductivity reach Tconset = 78.2 K and Tczero = 40 K at 15 GPa. This superconducting phase shares the samilar structural symmetry as many cuprate superconductors, providing a fresh platform to investigate underlying mechanisms of nickelate superconductors.Comment: 19 pages and 6 figure

Transient dynamics of terrestrial carbon storage : mathematical foundation and its applications

Terrestrial ecosystems have absorbed roughly 30 % of anthropogenic CO2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is time-dependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, which is the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times. Moreover, this and our other studies have demonstrated that one matrix equation can replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a three-dimensional (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical emulators make data assimilation computationally feasible so that both C flux- and pool-related datasets can be used to better constrain model predictions of land C sequestration. Overall, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.This work was partially done through the working group, Nonautonomous Systems and Terrestrial Carbon Cycle, at the National Institute for Mathematical and Biological Synthesis, an institute sponsored by the National Science Foundation, the US Departmernt of Homeland Security, and the US Department of Agriculture through NSF award no. EF-0832858, with additional support from the University of Tennessee, Knoxville, Research in Yiqi Luo EcoLab was financially supported by US Department of Energy grants DE-SC0008270, DE-SC0014085, and US National Science Foundation (NSF) grants EF 1137293 and OIA-1301789.Ye

Microbial carbon use efficiency promotes global soil carbon storage

Soils store more carbon than other terrestrial ecosystems$^{1,2}$. How soil organic carbon (SOC) forms and persists remains uncertain$^{1,3}$, which makes it challenging to understand how it will respond to climatic change$^{3,4}$. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss$^{5–7}$. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways$^{4,6,8–11}$, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes$^{12,13}$. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved$^{7,14,15}$. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate

Microbial carbon use efficiency promotes global soil carbon storage

Funding Information: We thank H. Yang, M. Schrumpf, T. Wutzler, R. Zheng and H. Ma for their comments and suggestions on this study. This work was supported by the National Natural Science Foundation of China (42125503) and the National Key Research and Development Program of China (2020YFA0608000, 2020YFA0607900 and 2021YFC3101600). F.T. was financially supported by China Scholarship Council during his visit at Food and Agricultural Organization of the United Nations (201906210489) and the Max-Planck Institute for Biogeochemistry (202006210289). The contributions of Y.L. were supported through US National Science Foundation DEB 1655499 and 2242034, subcontract CW39470 from Oak Ridge National Laboratory (ORNL) to Cornell University, DOE De-SC0023514, and the USDA National Institute of Food and Agriculture. S.M. has received funding from the ERC under the European Union’s H2020 Research and Innovation Programme (101001608). The contributions of U.M. were supported through a US Department of Energy grant to the Sandia National Laboratories, which is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the US Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. We thank the WoSIS database ( https://www.isric.org/explore/wosis ) for providing the publicly available global-scale SOC database used in this study. Publisher Copyright: © 2023, The Author(s).Peer reviewedPublisher PD