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Accurate organ segmentation is a fundamental step in disease-assisting diagnostic systems, and the precise segmentation of lung is crucial for subsequent lesion detection. Prior to this, lung segmentation algorithms had typically segmented the entire lung tissue. However, the trachea is also essential for diagnosing lung diseases. Challenges in lung parenchyma segmentation include the limited robustness of U-Net in acquiring contextual information and the small size of the trachea being mixed up with lung, making it difficult to identify and reconstruct the lungs. To overcome these difficulties, this paper proposes three improvements to U-Net: multiple concatenation modules to enhance the network's ability to capture context, multi-scale residual learning modules to improve the model's multi-scale learning capabilities, and an enhanced gated attention mechanism to enhance the fusion of various hierarchical features. The experimental results demonstrate that our model has achieved a significant improvement in trachea segmentation compared to existing models. [ABSTRACT FROM AUTHOR]

The visual object tracking technology of remote sensing images has important applications in areas with high safety performance such as national defense, homeland security, and intelligent transportation in smart cities. However, previous research demonstrates that adversarial examples pose a significant threat to remote sensing imagery. This article first explores the impact of adversarial examples in the field of visual object tracking in remote sensing imagery. We design a classification- and regression-based loss function for the popular Siamese RPN series of visual object tracking models and use the PGD gradient-based attack method to generate adversarial examples. Additionally, we consider the temporal consistency of video frames and design an adversarial examples attack method based on momentum continuation. We evaluate our method on the remote sensing visual object tracking datasets SatSOT and VISO and the traditional datasets OTB100 and UAV123. The experimental results show that our approach can effectively reduce the performance of the tracker. [ABSTRACT FROM AUTHOR]

The c-axis aligned crystalline indium-gallium-zinc-oxide field-effect transistor (CAAC-IGZO FET), exhibiting an extremely low off-state leakage current (~10−22 A/μm), has promised to be an ideal candidate for Dynamic Random Access Memory (DRAM) applications. However, the instabilities leaded by the drift of the threshold voltage in various stress seriously affect the device application. To better develop high performance CAAC-IGZO FET for DRAM applications, it's essential to uncover the deep physical process of charge transport mechanism in CAAC-IGZO FET. In this work, by combining the first-principles calculations and nonradiative multiphonon theory, the charge trapping and emission properties in CAAC-IGZO FET have been systematically investigated. It is found that under positive bias stress, hydrogen interstitial in Al2O3 gate dielectric is probable effective electron trap center, which has the transition level (ε (+1/−1) = 0.52 eV) above Fermi level. But it has a high capture barrier about 1.4 eV and low capture rate. Under negative bias stress, oxygen vacancy in Al2O3 gate dielectric and CAAC-IGZO active layer are probable effective electron emission centers whose transition level ε (+2/0) distributed at −0.73~−0.98 eV and 0.69 eV below Fermi level. They have a relatively low emission barrier of about 0.5 eV and 0.25 eV and high emission rate. To overcome the instability in CAAC-IGZO FET, some approaches can be taken to control the hydrogen concentration in Al2O3 dielectric layer and the concentration of the oxygen vacancy. This work can help to understand the mechanisms of instability of CAAC-IGZO transistor caused by the charge capture/emission process. [ABSTRACT FROM AUTHOR]

The continued scaling-down of nanoscale semiconductor devices has made it very challenging to obtain analytic surface potential solutions from complex equations in physics, which is the fundamental purpose of the MOSFET compact model. In this work, we proposed a general framework to automatically derive analytical solutions for surface potential in MOSFET, by leveraging the universal approximation power of deep neural networks. Our framework incorporated a physical-relation-neural-network (PRNN) to learn side-by-side from a general-purpose numerical simulator in handling complex equations of mathematical physics, and then instilled the "knowledge" from the simulation data into the neural network, so as to generate an accurate closed-form mapping between device parameters and surface potential. Inherently, the surface potential was able to reflect the numerical solution of a two-dimensional (2D) Poisson equation, surpassing the limits of traditional 1D Poisson equation solutions, thus better illustrating the physical characteristics of scaling devices. We obtained promising results in inferring the analytic surface potential of MOSFET, and in applying the derived potential function to the building of 130 nm MOSFET compact models and circuit simulation. Such an efficient framework with accurate prediction of device performances demonstrates its potential in device optimization and circuit design. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic threatens global food security and has created an urgent need for food conservation. This article presents a review of clean plate campaigns around the world. It aims to fight food waste and reveal the factors that may influence food waste. The Clean Plate Club in the US developed during wartime and relied heavily on political power for compliance, whereas the Clean Plate movement in South Korea was based on religion. China's Clean Your Plate Campaign (CYPC) has gone through two stages: CYPC I and CYPC II. The latter occurred during the unstable period of the COVID-19 pandemic. It was large-scale and more strongly enforced than CYPC I. In China, CYPC has relied more on personal virtue than on politics or religion. Culture, policy, COVID-19, and behavior are all important social factors that influence food waste. Specifically, two cultural values are drivers of food waste in China: hospitality and face-saving (mianzi). In terms of policy, China's food waste law mainly relies on persuasion; it lacks any power of enforcement. Laws in France and Italy, by contrast, focus on re-using food and involve both coercion and incentives. COVID-19 may have led to panic purchasing and stockpiling, but, in general, it has resulted in a reduction in food waste. [ABSTRACT FROM AUTHOR]

Assessing the effects of future land use and land cover change (LULC) on ecological processes and functions is crucial for improving regional sustainability in arid and semiarid areas. Taking the Agro-Pastoral Transitional Zone of Northern China (APTZNC) as an example, four IPCC Special Report on Emissions Scenarios scenarios (Scenario of economic emphasis on a regional scale (A1B), Scenario of economic emphasis on a global scale (A2), Scenario of environmental protection on a regional scale (B1), Scenario of environmental protection on a global scale (B2)) were adopted in the study to analyze the influence of the future land use and land cover change on the net primary production (NPP), soil organic matter (SOM), soil total nitrogen (TN), and soil erosion (ERO) using the model of Terrestrial Ecosystem Simulator-Land use/land cover model (TES-LUC) linking ecological processes and land-use change dynamics. The results were analyzed from the perspectives of LULC components, LULC conversions, and landscape patterns under the four scenarios. The main results include the following: (1) Environmentally oriented scenarios (A1B and B1) experienced the conservation of forest and grassland; economically oriented scenarios (A2 and B2) were characterized by significant loss of natural land covers and expansion of agricultural and urban land uses. (2) The NPP and soil nutrients are the highest while the ERO is the lowest in the woodland; the trend in cultivated land is opposite to that in woodland; the grassland ecosystem function is relatively stable and could make an important contribution to effectively mitigate global climate change. (3) The general trend in NPP, SOM, and TN under the four scenarios is B1 > A1B > baseline (2010) > B2 > A2, and that in ERO is A2 > B2 > baseline (2010) > A1B > B1. (4) Trade-offs between ecosystem functions and the ecological effects of LULC can be evaluated and formulated into decision-making. [ABSTRACT FROM AUTHOR]

Studies on land use structural optimization can support the sustainability of land resources. The Taipusi Banner lies in the arid and semiarid area of northern China, with rapid economic development and a vulnerable ecological condition. Taking the Taipusi Banner as a research case, we adopted a land use map and statistical data, and employed the ecosystem process model to establish five scenarios, including an economically optimal scenario, an ecologically optimal scenario, a comprehensively optimal scenario, a status quo, and a projected scenario. Based on multi-objective linear programming, the land use demand was optimized; then, the CLUE-S model and adaptability evaluation were adopted to establish spatial patterns. The ecological and economic benefits were then analyzed and policy suggestions are provided. The main results include the following: (1) The optimization outputs of various scenarios show that under optimization, cropland and forestland increased by 9.13% and 18.9%, respectively, and grassland decreased by 9.81%. (2) The land use optimization shows that comprehensive optimization aimed at achieving comprehensive benefits, ecological benefits, and economic benefits increased these benefits by 3.89%, 2.1%, and 6.2%, respectively. Compared with other scenarios, focusing on the comprehensive benefits of land use can result in the greatest increase in benefits to improve sustainability land resources. Land use optimization must consider not only the optimization of both the quantity and configuration but also the dimensions of both ecology and the economy. Land use should be based on a land suitability evaluation and optimization of the land use spatial configuration to update ineffective land uses and should gradually adjust both the ecological and engineering measures. [ABSTRACT FROM AUTHOR]

Climatic, socio-economic, geophysical, and human activity factors, among others, influence land use patterns. However, these driving factors also have different relationships with each other. Combining machine learning methods and statistical models is a good way to simulate the dominant land use types. The Luan River basin is located in a farming-pastoral transitional zone and is an important ecological barrier between Beijing and Tianjin. In this study, we predicted future land use and land cover changes from 2010 to 2020 in the Luan River's upper and middle reaches under three scenarios—the natural scenario, the ecological scenario, and the sustainable scenario. The results indicate that cultivated land will decrease while the forested areas will increase quantitatively in the future. Built-up areas would increase quickly in the natural scenario, and augmented expansion of forest would be the main features of land use changes in both the ecological scenario and the sustainable scenario. Regarding the spatial pattern, different land use patterns will be aggregated and patches will become larger. Our findings for the scenario analysis of land use changes can provide a reference case for sustainable land use planning and management in the upper and middle Luan River basin. [ABSTRACT FROM AUTHOR]

Land use/land cover changes (LULCC) have been affected by ecological processes as well as socioeconomic and human activities, resulting in several environmental problems. The study of the human–environment system combined with land use/land cover dynamics has received considerable attention in recent decades. We aimed to provide an integrated model that couples land use, socioeconomic influences, and ecosystem processes to explore the future dynamics of land use under two scenarios in China. Under Scenario A, the yield of grain continues to increase, and under Scenario B, the yield of grain remains constant. This study created a LULCC model by integrating a simple global socioeconomic model, a Terrestrial ecosystem simulator (TESim), and a land use allocation model. The results were analyzed by comparing spatiotemporal differences under predicted land use conditions in the two alternative scenarios. The simulation results showed patterns that varied between the two scenarios. In Scenario A, grassland will expand in the future and a large reduction in cropland will be observed. In Scenario B, the augmented expansion of cropland and a drastic shrinkage of forest area will be the main land use conversion features. Scenario A is more promising because more land is preserved for ecological restoration and urbanization, which is in line with China's Grain for Green Program. Economic development should be based on ecological protection. The results are expected to add insight to sustainable land use development and regional natural resource management in China. [ABSTRACT FROM AUTHOR]

Transcription control plays a crucial role in establishing a unique gene expression signature for each of the hundreds of mammalian cell types. Though gene expression data have been widely used to infer cellular regulatory networks, existing methods mainly infer correlations rather than causality. We developed statistical models and likelihood-ratio tests to infer causal gene regulatory networks using enhancer RNA (eRNA) expression information as a causal anchor and applied the framework to eRNA and transcript expression data from the FANTOM Consortium. Predicted causal targets of transcription factors (TFs) in mouse embryonic stem cells, macrophages and erythroblastic leukaemia overlapped significantly with experimentally-validated targets from ChIP-seq and perturbation data. We further improved the model by taking into account that some TFs might act in a quantitative, dosage-dependent manner, whereas others might act predominantly in a binary on/off fashion. We predicted TF targets from concerted variation of eRNA and TF and target promoter expression levels within a single cell type, as well as across multiple cell types. Importantly, TFs with high-confidence predictions were largely different between these two analyses, demonstrating that variability within a cell type is highly relevant for target prediction of cell type-specific factors. Finally, we generated a compendium of high-confidence TF targets across diverse human cell and tissue types. [ABSTRACT FROM AUTHOR]

Considering the widespread occurrence of oxalate in nature and its broad impact on a host of organisms, it is surprising that so little is known about the turnover of this important acid. In plants, oxalate oxidase is the most well-studied enzyme capable of degrading oxalate, but not all plants possess this activity. Recently, acyl-activating enzyme 3 (AAE3), encoding an oxalyl-CoA synthetase, was identified in Arabidopsis. This enzyme has been proposed to catalyze the first step in an alternative pathway of oxalate degradation. Since this initial discovery, this enzyme and proposed pathway have been found to be important to other plants and yeast as well. In this study, we identify, in Arabidopsis, an oxalyl-CoA decarboxylase (AtOXC) that is capable of catalyzing the second step in this proposed pathway of oxalate catabolism. This enzyme breaks down oxalyl-CoA, the product of AtAAE3, into formyl-CoA and CO 2 . AtOXC:GFP localization suggested that this enzyme functions within the cytosol of the cell. An Atoxc knock-down mutant showed a reduction in the ability to degrade oxalate into CO 2 . This reduction in AtOXC activity resulted in an increase in the accumulation of oxalate and the enzyme substrate, oxalyl-CoA. Size exclusion studies suggest that the enzyme functions as a dimer. Computer modeling of the AtOXC enzyme structure identified amino acids of predicted importance in co-factor binding and catalysis. Overall, these results suggest that AtOXC catalyzes the second step in this alternative pathway of oxalate catabolism.