In this paper, we present a new deep learning framework for 3-D tomographic reconstruction. To this end, we map filtered back-projection-type algorithms to neural networks. However, the ...back-projection cannot be implemented as a fully connected layer due to its memory requirements. To overcome this problem, we propose a new type of cone-beam back-projection layer, efficiently calculating the forward pass. We derive this layer's backward pass as a projection operation. Unlike most deep learning approaches for reconstruction, our new layer permits joint optimization of correction steps in volume and projection domain. Evaluation is performed numerically on a public data set in a limited angle setting showing a consistent improvement over analytical algorithms while keeping the same computational test-time complexity by design. In the region of interest, the peak signal-to-noise ratio has increased by 23%. In addition, we show that the learned algorithm can be interpreted using known concepts from cone beam reconstruction: the network is able to automatically learn strategies such as compensation weights and apodization windows.
The Li metal is an ideal anode material owing to its high theoretical specific capacity and low electrode potential. However, its high reactivity and dendritic growth in carbonate-based electrolytes ...limit its application. To address these issues, we propose a novel surface modification technique using heptafluorobutyric acid. In-situ spontaneous reaction between Li and the organic acid generates a lithiophilic interface of lithium heptafluorobutyrate for dendrite-free uniform Li deposition, which significantly improves the cycle stability (Li/Li symmetric cells >1200 h at 1.0 mA cm
) and Coulombic efficiency (>99.3%) in conventional carbonate-based electrolytes. This lithiophilic interface also enables full batteries to achieve 83.2% capacity retention over 300 cycles under realistic testing condition. Lithium heptafluorobutyrate interface acts as an electrical bridge for uniform lithium-ion flux between Li anode and plating Li, which minimizes the occurrence of tortuous lithium dendrites and lowers interface impedance.
The use of natural substance to ward off microbial infections has a long history. However, the large-scale production of natural extracts often reduces antibacterial potency, thus limiting practical ...applications. Here we present a strategy for converting natural organosulfur compounds into nano-iron sulfides that exhibit enhanced antibacterial activity. We show that compared to garlic-derived organosulfur compounds nano-iron sulfides exhibit an over 500-fold increase in antibacterial efficacy to kill several pathogenic and drug-resistant bacteria. Furthermore, our analysis reveals that hydrogen polysulfanes released from nano-iron sulfides possess potent bactericidal activity and the release of polysulfanes can be accelerated by the enzyme-like activity of nano-iron sulfides. Finally, we demonstrate that topical applications of nano-iron sulfides can effectively disrupt pathogenic biofilms on human teeth and accelerate infected-wound healing. Together, our approach to convert organosulfur compounds into inorganic polysulfides potentially provides an antibacterial alternative to combat bacterial infections.
Earth observation satellite task scheduling research plays a key role in space-based remote sensing services. An effective task scheduling strategy can maximize the utilization of satellite resources ...and obtain larger objective observation profits. In this paper, inspired by the success of deep reinforcement learning in optimization domains, the deep deterministic policy gradient algorithm is adopted to solve a time-continuous satellite task scheduling problem. Moreover, an improved graph-based minimum clique partition algorithm is proposed for preprocessing in the task clustering phase by considering the maximum task priority and the minimum observation slewing angle under constraint conditions. Experimental simulation results demonstrate that the deep reinforcement learning-based task scheduling method is feasible and performs much better than traditional metaheuristic optimization algorithms, especially in large-scale problems.
Introduction: The importance of harmonious human-land dynamics and the practical value of investigating the interplay between demographic urbanization and land development. Methods: The study area ...(110 prefecture-level cities within the Yangtze River Economic Belt) and the integrative methodology employed (coupled coordination model, exploratory spatial data analysis, and fixed effects model). Results: The main findings, including the progression in the coupled coordination level, the spatial gradient, and the factors influencing the coupled coordination level. Discussion: Recommendations for surmounting institutional and policy impediments, fostering resource exchange, and facilitating the integrated evolution of human-land dynamics within the region.
Flood routing is a critical component of real‐time flood forecasting and plays a pivotal role in reducing economic and life losses due to flooding. However, flood routing always possesses errors ...because it is affected by various sources of uncertainty, which are transmitted and accumulated in the routing process from upstream to downstream, particularly for long or complex river systems. Thus, a real‐time error correction is rapidly becoming a key measure for reducing errors and improving flood forecasting accuracy. In this study, a dynamic system inversion (DSI) model was developed as a new correction method to update forecasting errors for long river systems. The new method utilizes all observed, forecasted, and corrected information at upstream stations and couples the matrix‐based Muskingum routing method with an error‐inversed updating technique to form the DSI equation. Multisource spatiotemporal errors of the river system are simultaneously combined into the DSI equation, and the discharges at the downstream station are reforecasted, thereby providing error correction in flood routing. Based on a case study on the Le'an River (China), the DSI model performed better than the traditional autoregressive (AR) model based on five metrics for both small and large flood events. Moreover, with an increase in lead time, the metrics of AR deteriorated rapidly, whereas that of DSI deteriorated slightly. In conclusion, the proposed DSI model is a sophisticated and robust method for flood routing and provides a new alternative for error correction in flood routing simulations of complex river systems.
Key Points
A dynamic system inversion model is developed to improve the accuracy of real‐time flood routing for complex or long river systems
The model couples the matrix‐based Muskingum routing method with an error‐inversed updating technique to correct forecasting errors
The model simultaneously corrects multisource spatiotemporal errors, providing a new alternative for error correction
N
6
-methyladenosine (m
6
A) on chromosome-associated regulatory RNAs (carRNAs), including repeat RNAs, plays important roles in tuning the chromatin state and transcription, but the intrinsic ...mechanism remains unclear. Here, we report that YTHDC1 plays indispensable roles in the self-renewal and differentiation potency of mouse embryonic stem cells (ESCs), which highly depends on the m
6
A-binding ability.
Ythdc1
is required for sufficient rRNA synthesis and repression of the 2-cell (2C) transcriptional program in ESCs, which recapitulates the transcriptome regulation by the LINE1 scaffold. Detailed analyses revealed that YTHDC1 recognizes m
6
A on LINE1 RNAs in the nucleus and regulates the formation of the LINE1-NCL partnership and the chromatin recruitment of KAP1. Moreover, the establishment of H3K9me3 on 2C-related retrotransposons is interrupted in
Ythdc1
-depleted ESCs and inner cell mass (ICM) cells, which consequently increases the transcriptional activities. Our study reveals a role of m
6
A in regulating the RNA scaffold, providing a new model for the RNA-chromatin cross-talk.
This paper selects the data of the lower Yangtze River Economic Belt from 2013 to 2017 as the reference. First, the location entropy method is used to measure the level of producer services ...agglomeration. Through the establishment of the SBM model, the input and output indicators are introduced to calculate the trend of economic green transformation in provinces and cities of lower Yangtze River economy. Finally, this paper takes the green efficiency value as the dependent variable and uses the five quantities such as the agglomeration degree of the producer service industry as independent variables to construct the Tobit model, and uses the MLE method to measure the variable coefficients to analyze the impact of the agglomeration of the producer service industry on the economic green transformation. The results show that in the lower Yangtze River Economic Belt, Shanghai, Jiangsu Province and Zhejiang Province have a good development trend in the green transition of the producer service industry agglomeration economy; and they all show the spatial distribution characteristics of "east high and west low"; producer services Industry agglomeration has a significant positive role in promoting green economic transformation.
The gut microbiota significantly influences the health and growth of red-spotted grouper (Epinephelus akaara), a well-known commercial marine fish from Fujian Province in southern China. However, ...variations in survival strategies and seasons can impact the stability of gut microbiota data, rendering it inaccurate in reflecting the state of gut microbiota. Which impedes the effective enhancement of aquaculture health through a nuanced understanding of gut microbiota. Inspired by this, we conducted a comprehensive analysis of the gut microbiota of wild and captive E. akaara in four seasons. Seventy-two E. akaara samples were collected from wild and captive populations in Dongshan city, during four different seasons. Four sections of the gut were collected to obtain comprehensive information on the gut microbial composition and sequenced using 16S rRNA next-generation Illumina MiSeq. We observed the highest gut microbial diversity in both captive and wild E. akaara during the winter season, and identified strong correlations with water temperature using Mantel analysis. Compared to wild E. akaara, we found a more complex microbial network in captive E. akaara, as evidenced by increased abundance of Bacillaceae, Moraxellaceae and Enterobacteriaceae. In contrast, Vibrionaceae, Clostridiaceae, Flavobacteriaceae and Rhodobacteraceae were found to be more active in wild E. akaara. However, some core microorganisms, such as Firmicutes and Photobacterium, showed similar distribution patterns in both wild and captive groups. Moreover, we found the common community composition and distribution characteristics of top 10 core microbes from foregut to hindgut in E. akaara. Collectively, the study provides relatively more comprehensive description of the gut microbiota in E. akaara, taking into account survival strategies and temporal dimensions, which yields valuable insights into the gut microbiota of E. akaara and provides a valuable reference to its aquaculture.
Inhomogeneous materials, variable foundations, non-uniform cross-sections, and non-uniformly distributed loads are common in engineering structures and typically complicate their mechanical analysis ...considerably. This paper presents an accurate and efficient numerical method for the dynamic analysis of non-uniform functionally graded beams resting on inhomogeneous viscoelastic foundations subjected to non-uniformly distributed moving load and investigates the effects of non-uniformities and inhomogeneities on material, foundation, and load. Based on the Timoshenko beam theory and a Chebyshev spectral method, a consistent discrete dynamic model is derived, which can deal with all axially varying properties. A series of numerical experiments are carried out to validate the convergence and accuracy of the proposed method. The results are compared with those obtained through finite element analysis or in the literature, and excellent agreement is observed. Then, the dynamic response of an axially functionally graded beam resting on an inhomogeneous viscoelastic foundation and subjected to a non-uniformly distributed moving load is investigated. The results show that the material gradient and the inhomogeneous foundation can alter the vibration amplitudes and critical speeds of the beam significantly. Compared with more realistic non-uniformly distributed moving load models, idealized concentrated and uniformly distributed moving load models produce apparent computation errors in vibration amplitudes.
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