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arXiv 2024年

作者： Moyal, Roy Mama, Kyrus R. Einhorn, Matthew Borthakur, Ayon Cleland, Thomas A. Computational Physiology Lab Department of Psychology Cornell University IthacaNY14853 United States AI for Science Institute Cornell University IthacaNY14853 United States Neurosciences Graduate Program Stanford University StanfordCA94305 United States Mehta Family School of Data Science and Artificial Intelligence IIT Guwahati Assam Guwahati781039 India

The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains are adept at learning stable representations given small samples of noisy observations;across sensory modalities, this capacity is aided by a cascade of signal conditioning steps informed by domain knowledge. The olfactory system, in particular, solves a source separation and denoising problem compounded by concentration variability, environmental interference, and unpredictably correlated sensor affinities. To function optimally, its plastic network requires statistically well-behaved input. We present a data-blind neuromorphic signal conditioning strategy whereby analog data are normalized and quantized into spike phase representations. Input is delivered to a column of duplicated spiking principal neurons via heterogeneous synaptic weights;this regularizes layer utilization, yoking total activity to the network’s operating range and rendering internal representations robust to uncontrolled open-set stimulus variance. We extend this mechanism by adding a data-aware calibration step whereby the range and density of the quantization weights adapt to accumulated input statistics, optimizing resource utilization by balancing activity regularization and information retention. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantization (signal)

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Decomposition of dynamic transcriptomic responses during effector-triggered immunity reveals conserved responses in two distinct plant cell populations

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Plant Communications 2024年第8期5卷 1-17页

作者： Xiaotong Liu Daisuke Igarashi Rachel A.Hillmer Thomas Stoddard You Lu Kenichi Tsuda Chad L.Myers Fumiaki Katagiri Department of Plant and Microbial Biology University of Minnesota-Twin CitiesSt PaulMN 55108USA Department of Computer Science and Engineering University of Minnesota-Twin CitiesMinneapolisMN 55455USA Bioinformatics and Computational Biology Graduate Program University of Minnesota-Twin CitiesMinneapolisMN 55455USA Institute for Innovation Ajinomoto Co.Inc.KawasakiJapan State Key Laboratory of Agricultural Microbiology Hubei Hongshan LaboratoryHubei Key Lab of Plant PathologyCollege of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan 430070China Present address:225 S 6th St MinneapolisMN 55402USA Present address:Biotalys Inc.2520 Meridian Parkway Suite 480DurhamNC 27713USA Present address:Life Sciences Institute and Department of Cell and Developmental Biology University of Michigan Medical SchoolAnn ArborMI 48109USA

Rapid plant immune responses in the appropriate cells are needed for effective defense against *** transcriptome analysis is often used to describe overall immune responses,collection of transcriptome data with sufficient resolution in both space and time is *** reanalyzed public Arabidopsis time-course transcriptome data obtained after low-dose inoculation with a Pseudomonas syringae strain expressing the effector AvrRpt2,which induces effector-triggered immunity in ***-peak time-course patterns are prevalent among thousands of upregulated *** implemented a multicompartment modeling approach to decompose the double-peak pattern into two single-peak patterns for each *** decomposed peaks reveal an“echoing”pattern:the peak times of the first and second peaks correlate well across most upregulated *** demonstrated that the two peaks likely represent responses of two distinct cell populations that respond either cell autonomously or indirectly to ***,the peak decomposition has extracted spatial information from the time-course *** echoing pattern also indicates a conserved transcriptome response with different initiation times between the two cell populations despite different elicitor types.A gene set highly overlapping with the conserved gene set is also upregulated with similar kinetics during pattern-triggered *** of a WRKY network via different entry-point WRKYs can explain the similar but not identical transcriptome responses elicited by different elicitor *** discuss potential benefits of the properties of the WRKY activation network as an immune signaling network in light of pressure from rapidly evolving pathogens.

关键词： Arabidopsis Pseudomonas syringae plant immune response multi-compartment model WRKY transcription factors resilient immune signaling network

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The recency and geographical origins of the bat viruses ancestral to SARS-CoV and SARS-CoV-2

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Cell 2025年

作者： Pekar, Jonathan E. Lytras, Spyros Ghafari, Mahan Magee, Andrew F. Parker, Edyth Wang, Yu Ji, Xiang Havens, Jennifer L. Katzourakis, Aris Vasylyeva, Tetyana I. Suchard, Marc A. Hughes, Alice C. Hughes, Joseph Rambaut, Andrew Robertson, David L. Dellicour, Simon Worobey, Michael Wertheim, Joel O. Lemey, Philippe Institute of Ecology and Evolution University of Edinburgh Edinburgh United Kingdom Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla 92093 CA United States Department of Biomedical Informatics University of California San Diego La Jolla 92093 CA United States Department of Medicine University of California San Diego La Jolla 92093 CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Medical Research Council University of Glasgow Centre for Virus Research Glasgow United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States Department of Immunology and Microbiology The Scripps Research Institute La Jolla 92037 CA United States Institute of Genomics and Global Health Redeemer's University Osun State Ede Nigeria Department of Computer Science and Engineering University of California San Diego La Jolla 92093 CA United States Department of Mathematics School of Science and Engineering Tulane University New Orleans LA United States Department of Population Health and Disease Prevention University of California Irvine Irvine 92617 CA United States Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles 90095 CA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles 90095 CA United States School of Biological Sciences University of Hong Kong Hong Kong Hong Kong China Biodiversity Green Development Foundation Beijing China Spatial Epidemiology Lab (SpELL) Université Libre de Bruxelles CP160/12 50 av. FD Roosevelt Bruxelles 1050 Belgium Department of Microbiology Immunology and Transplantation Rega Institute

The emergence of SARS-CoV in 2002 and SARS-CoV-2 in 2019 led to increased sampling of sarbecoviruses circulating in horseshoe bats. Employing phylogenetic inference while accounting for recombination of bat sarbecoviruses, we find that the closest-inferred bat virus ancestors of SARS-CoV and SARS-CoV-2 existed less than a decade prior to their emergence in humans. Phylogeographic analyses show bat sarbecoviruses traveled at rates approximating their horseshoe bat hosts and circulated in Asia for millennia. We find that the direct ancestors of SARS-CoV and SARS-CoV-2 are unlikely to have reached their respective sites of emergence via dispersal in the bat reservoir alone, supporting interactions with intermediate hosts through wildlife trade playing a role in zoonotic spillover. These results can guide future sampling efforts and demonstrate that viral genomic regions extremely closely related to SARS-CoV and SARS-CoV-2 were circulating in horseshoe bats, confirming their importance as the reservoir species for SARS viruses. © 2025 The Authors

关键词： molecular epidemiology phylogeography sarbecoviruses virus evolution

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A Method for Automatic Generation of 3D Background Models Using Fractal Models and Genetic Algorithms

A Method for Automatic Generation of 3D Background Models Us...

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International Symposium on Computing and Networking Workshops (CANDARW)

作者： Kaito Watanabe Hirotaka Nakayama Atsushi Shiraki Tomoyoshi Ito Katsumi Hirata Naoto Hoshikawa Advanced Course General Engineering Program National Institute of Technology Oyama College Oyama Japan Center for Computational Astrophysics National Astronomical Observatory of Japan Mitaka Japan Graduate School of Engineering Chiba University Chiba Japan Department of Innovative Electrical and Electronic Engineering National Institute of Technology Oyama College Oyama Japan Graduate School of Science and Tecnology University of Tsukuba Tsukuba Japan

In recent years, 3DCG-related industries have grown rapidly and the demand for 3DCG content has increased. However, 3DCG production requires special skills and a lot of time. In particular, the creation of background data requires a lot of human resources at the production site because the data is huge and complex. Therefore, we propose an automatic creation system that generates 3D background data for 3DCG production. The proposed system consists of three functions: 3D model generation, generation parameter optimization, and fitness evaluation. The user inputs a landscape photograph as idea information into the system, and the system optimizes the generation parameters so that the 3D object shape approximates the input image. To evaluate the accuracy of the system's performance, we observe the optimization process and the 3D object output when a previously generated fractal image or landscape photos are input. Experimental results show that the system accurately optimizes the generation parameters and generates 3D objects that are almost identical to the shape of the fractal image. In addition, the parameters were also optimized with a certain degree of accuracy for landscape photographs for which no specific solution had been determined.

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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Data science and Engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Shun Cheng Chang Chien Fu Liu Marta Misztal Yi Hsien Hsieh Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London UK Department of Electrical Engineering NTU Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taiwan Smart Medicine and Health Informatics Program NTU Taiwan

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients.

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Ultrafast asymmetric Rosen-Zener-like coherent phonon responses observed in silicon

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Physical Review B 2019年第17期99卷 174304-174304页

作者： Yohei Watanabe Ken-ichi Hino Nobuya Maeshima Hrvoje Petek Muneaki Hase Doctoral Program in Materials Science Graduate School of Pure and Applied Sciences University of Tsukuba Tsukuba Ibaraki 305-8573 Japan Division of Materials Science Faculty of Pure and Applied Sciences University of Tsukuba Tsukuba 305-8573 Japan Center for Computational Sciences University of Tsukuba Tsukuba 305-8577 Japan Department of Physics and Astronomy and Pittsburgh Quantum Institute University of Pittsburgh Pittsburgh Pennsylvania 15260 USA Division of Applied Physics Faculty of Pure and Applied Sciences University of Tsukuba Tsukuba 305-8573 Japan

We investigate the spectral profiles of time signals attributed to coherent phonon generation in an undoped Si crystal. Here, the retarded longitudinal-optical- (LO-) phonon Green function relevant to the temporal variance of induced charge density of ionic cores is calculated by employing the polaronic quasiparticle model developed by the authors [Y. Watanabe et al., Phys. Rev. B 95, 014301 (2017); Phys. Rev. B 96, 125204 (2017)]. A spectral asymmetry is revealed in the frequency domain of the signals under the condition that an LO-phonon mode stays almost energetically resonant with a plasmon mode in the early time region; this lasts for approximately 100 fs immediately after the irradiation of an ultrashort pump-laser pulse. It is understood that based on the adiabatic picture in time, this asymmetry is caused by the Rosen-Zener coupling between both modes. The associated experimental results are obtained by measuring time-dependent electro-optic reflectivity signals, and it is proved that these are in harmony with the calculated ones. The spectra become more symmetric, as the photoexcited carrier density further changes from that meeting the above condition to higher and lower sides of carrier densities. Moreover, the effect of optical nutation of carrier density on the coherent phonon signals is addressed, and the present results are compared with the asymmetry caused by transient Fano resonance and the spectral profiles observed in a GaAs crystal.

关键词： Irradiation effects Optical phonons Photoinduced effect Elemental semiconductors Femtosecond laser irradiation Reflectivity

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Effect of chemical and biological additives on production of biogas from coffee pulp silage

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Scientific Reports 2023年第1期13卷 1-11页

作者： Getachew, Mengizam Tsegaye Hiruy, Andualem Mekonnen Mazharuddin, Majid Mohiuddin Mamo, Tadios Tesfaye Feseha, Temesgen Aragaw Mekonnen, Yedilfana Setarge Center for Environmental Science College of Natural and Computational Sciences Addis Ababa University Addis Ababa Ethiopia Institute of Atomic and Molecular Sciences Academia Sinica Taipei Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica Taipei 11529 Taiwan Institute of Physics Academia Sinica Taipei 11529 Taiwan Department of Chemistry National Taiwan University Taipei 10617 Taiwan

Energy is the foundation of the global economy and is essential to human survival. Nevertheless, more than 60% of it comes from fossil fuels. That is not a replenished and scarce source. However, a sizable amount of organic waste is generated every minute throughout the world and can be used as a raw material to produce renewable energy. Among them, Coffee processing generates a huge amount of solid and liquid waste that is organic and can serve as raw material for biofuel production. Since coffee beans and powder are Ethiopia’s main exports, coffee pulp is easily accessible. Therefore, the main goal of this project is to convert this waste, which largely consists of organic materials, into a valuable product called Methane. The purity and yield of methane productivity are significantly influenced by the type of additives we use. This work systematically investigates the effect of chemical and biological additives on the productivity and purity of the Biogas from the coffee pulp silage in batch systems under mesophilic temperature (38 °C) for different ensiling periods and additive proportions. The chemical additives recorded the maximum biogas production (2980 ml) at an ensiling period of 40 days with high purity of about 70% biogas. The minimum Biogas was recorded at the ensiling period of 10 days by the control (T1) treatments, which was 634 ml. This work proves that biological additives produced the highest quality and quantity of Biogas from coffee silage. © 2023, The Author(s).

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Convolutional neural networks for automated annotation of cellular cryo-electron tomograms

arXiv

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arXiv 2017年

作者： Chen, Muyuan Dai, Wei Sun, Stella Y. Jonasch, Darius He, Cynthia Y. Schmid, Michael F. Chiu, Wah Ludtke, Steven J. Graduate Program in Structural and Computational Biology and Molecular Biophysics Baylor College of Medicine HoustonTX77030 United States Verna Marrs and McLean Department of Biochemistry and Molecular Biology Baylor College of Medicine HoustonTX77030 United States Department of Biological Science Centre for BioImaging Sciences National University of Singapore 14 Science Drive 4 Singapore117543 Singapore

Cellular Electron Cryotomography (CryoET) offers the ability to look inside cells and observe macromolecules frozen in action. A primary challenge for this technique is identifying and extracting the molecular components within the crowded cellular environment. We introduce a method using neural networks to dramatically reduce the time and human effort required for subcellular annotation and feature extraction. Subsequent subtomogram classification and averaging yields in-situ structures of molecular components of interest. © 2017, CC BY.

关键词： Cellular neural networks

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Comparing LSTM and BLSTM Deep Neural Networks for Power Consumption Prediction: Preliminary studies

arXiv

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arXiv 2023年

作者： da Silva, Davi Guimarães de Moura Meneses, Anderson Alvarenga Federal University of Western Pará – Graduate Program in Society Nature and Development R. Vera Paz s/n Salé PA SantarémCEP 68.035-110 Brazil Federal Institute of Education Science and Technology of Pará Brazil Federal University of Western Pará Institute of Geosciences and Engineering Laboratory of Computational Intelligence Brazil

Electric consumption prediction methods are investigated for many reasons such as decision-making related to energy efficiency as well as for anticipating demand in the energy market dynamics. The objective of the present work is the comparison between two Deep Learning models, namely the Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM) for univariate electric consumption Time Series (TS) short-term forecast. The Data Sets (DSs) were selected for their different contexts and scales, aiming the assessment of the models’ robustness. Four DSs were used, related to the power consumption of: (a) a household in France;(b) a university building in Santarém, Brazil;(c) the Tétouan city zones, in Morocco;and (c) the Singapore aggregated electric demand. The metrics RMSE, MAE, MAPE and R2 were calculated in a TS cross-validation scheme. The Friedman’s test was applied to normalized RMSE (NRMSE) results, showing that BLSTM outperforms LSTM with statistically significant difference (p = 0.0455), corroborating the fact that bidirectional weight updating improves significantly the LSTM performance concerning different scales of electric power consumption. © 2023, CC BY-NC-ND.

关键词： Long short-term memory

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Enhancing Sustainable Development Goals Through Future Vapor Pressure Deficit Analysis In The Nile River Basin

Enhancing Sustainable Development Goals Through Future Vapor...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Surendra Maharjan Wenzhao Li Shahryar Fazli Hani Sewilam Hesham El-Askary Computational and Data Sciences Graduate Program Schmid College of Science and Technology Chapman University Orange CA USA Earth Systems Science and Data Solutions Lab Chapman University Orange CA USA The Center for Applied Research on the Environment and Sustainability The American University in Cairo Cairo Egypt Department of Engineering Hydrology RWTH Aachen University Aachen Germany Department of Environmental Sciences Faculty of Science Alexandria University Alexandria Egypt

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Vapor Pressure Deficit (VPD) is crucial in meteorology and agriculture for understanding plant-environment interactions. Its application as an indicator in agricultural practices notably advances Sustainable Development Goals such as Zero Hunger (SDG 2) and Climate Action (SDG 13). This research focuses on the impact of climate change on agricultural productivity and food security in the Nile River Basin (NRB), emphasizing the role of VPD, temperature, and precipitation. Utilizing Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets from NEX-GDDP-CMIP6, the study analyzes key climatic variables that influence agricultural conditions. The study applies the Mann-Kendall test to evaluate VPD trends from 2000 to 2060 under two Shared Socioeconomic Pathways (SSPs), SSP2-4.5 and SSP5-8.5. The study's findings on the implications of rising VPD levels in the Nile River Basin (NRB), particularly under the SSP 5-8.5 scenario, highlight a critical challenge for the region's agricultural productivity and food security. The increased VPD, indicative of drier conditions, leads to a moisture deficit for crops, potentially reducing agricultural yields. This scenario poses a significant threat to food security, as lower crop yields can result in food shortages and higher food prices, adversely affecting vulnerable populations. The study underscores the necessity of integrating VPD insights into agricultural and water resource management strategies to uphold food security against climatic variations in support of the SDGs.

关键词： Productivity Climate change Precipitation Food security Agriculture Rivers Sustainable development

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