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arXiv 2022年

作者： Cui, Tiangang Dong, Jing Jasra, Ajay Tong, Xin T. School of Mathematics Monash University Australia Graduate School of Business Columbia University United States Applied Mathematics and Computational Science Program Computer Electrical Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Department of Mathematics National University of Singapore Singapore

When implementing Markov Chain Monte Carlo (MCMC) algorithms, perturbation caused by numerical errors is sometimes inevitable. This paper studies how perturbation of MCMC affects the convergence speed and Monte Carlo estimation accuracy. Our results show that when the original Markov chain converges to stationarity fast enough and the perturbed transition kernel is a good approximation to the original transition kernel, the corresponding perturbed sampler has similar convergence speed and high approximation accuracy as well. We discuss two different analysis frameworks: ergodicity and spectral gap, both are widely used in the literature. Our results can be easily extended to obtain non-asymptotic error bounds for MCMC estimators. We also demonstrate how to apply our convergence and approximation results to the analysis of specific sampling algorithms, including Random walk Metropolis and Metropolis adjusted Langevin algorithm with perturbed target densities, and parallel tempering Monte Carlo with perturbed densities. Finally we present some simple numerical examples to verify our theoretical claims. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse problems

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Linkages of the 2022 Unprecedented Global Heatwave Events to Triple-Dip La Niña

Linkages of the 2022 Unprecedented Global Heatwave Events to...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sachi Perera Joshua B. Fisher Mohamed Allali 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 Solution Lab Chapman University Orange CA USA Schmid College of Science and Technology Chapman University Orange CA USA Fowler School of Engineering Chapman University Orange CA USA Department of Environmental Sciences Faculty of Science Alexandria University Alexandria Egypt

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Heatwaves are influenced significantly by El Niño-Southern Oscillation (ENSO), which alters temperature and precipitation patterns throughout the world. Since 2020, we have witnessed a "triple dip" La Niña conditions persisting for three consecutive years resulting in severe weather and climate driven events globally. In this study, we identified the dominant frequency of Niño 3.4 Sea Surface Temperature (SST) signals and correlated them with regions experiencing unprecedented heat waves in 2022, namely, the Indian Ocean, the North Atlantic around England and Spain, and the Mediterranean Sea. The signal's power spectrum and its three highest power components are determined based on the signal's Singular Spectrum Analysis (SSA) for each region. Furthermore, the power spectrum coherence (PSC) of Niño 3.4 and other study regions is obtained to determine whether ENSO and heatwaves are linked. We find that Niño 3.4 and the Indian Ocean have the same dominant frequency for the highest power received, indicating ENSO's influence on the Indian heat wave. Additionally, we discovered that in March 2022, the Jet stream is essential in bringing warm Arctic waves southward, passing through Niño 3.4 and India. Furthermore, we found that no other region shared the dominant frequency of the Niño 3.4 region.

关键词： Heating systems Couplings Sea surface Precipitation Surface waves Indian Ocean Geoscience and remote sensing

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Correction: Spatial organization of B lymphocytes and prognosis prediction in patients with gastric cancer

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Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association 2025年 2025 May 16页

作者： Ryan Yong Kiat Tay Manavi Sachdeva Haoran Ma Young-Woo Kim Myeong-Cherl Kook Hyunki Kim Jae-Ho Cheong Lindsay C Hewitt Katharina Nekolla Günter Schmidt Takaki Yoshikawa Takashi Oshima Tomio Arai Supriya Srivastava Ming Teh Xuewen Ong Su Ting Tay Taotao Sheng Joseph J Zhao Patrick Tan Heike I Grabsch Raghav Sundar Yong Loo Lin School of Medicine National University of Singapore 1E Kent Ridge Road Singapore 119228 Singapore. Department of Haematology-Oncology National University Cancer Institute National University Hospital Singapore Singapore. Cancer and Stem Cell Biology Program Duke-NUS Medical School Singapore Singapore. Department of Cancer Policy and Population Health National Cancer Center Graduate School of Cancer Science and Policy and Center for Gastric Cancer and Department of Surgery National Cancer Center Goyang Republic of Korea. Center for Gastric Cancer Department of Pathology National Cancer Center Goyang Republic of Korea. Department of Pathology Yonsei University College of Medicine Seoul Republic of Korea. Department of Surgery Yonsei University College of Medicine Seoul Republic of Korea. Department of Pathology GROW Research Institute for Oncology and Reproduction Maastricht University Medical Center+ Maastricht The Netherlands. Department of Precision Medicine GROW School for Oncology and Reproduction Maastricht University Center+ Maastricht The Netherlands. Computational Pathology Oncology R&D AstraZeneca Munich Germany. Department of Surgery Kanagawa Cancer Center Yokohama Japan. Department of Surgery Yokohama City University Yokohama Japan. Department of Surgery Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital Tokyo Japan. Department of Pathology Tokyo Metropolitan Institute for Geriatrics and Gerontology Tokyo Japan. Department of Medicine National University of Singapore Singapore Singapore. Department of Pathology National University Hospital Singapore Singapore. Genome Institute of Singapore Agency for Science Technology and Research Singapore Singapore. Cancer Science Institute of Singapore National University of Singapore Singapore Singapore. Cellular and Molecular Research National Cancer Centre Singapore Singapore. Singhealth/Duke-NUS Institute of Precision Medicine National Heart Centre Singapore

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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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Tumor-derived interleukin-1 receptor antagonist exhibits immunosuppressive functions and promotes pancreatic cancer

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Cell and Bioscience 2023年第1期13卷 1页

作者： Fan, Yu-Ching Fong, Yu-Cin Kuo, Chun-Tse Li, Chia-Wei Chen, Wei-Yu Lin, Jian-Da Bürtin, Florian Linnebacher, Michael Bui, Quoc Thang Lee, Kuan-Der Tsai, Yuan-Chin Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University and Academia Sinica Taipei Taiwan Graduate Institute of Cancer Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan Institute of Biomedical Sciences Academia Sinica Taipei Taiwan Department of Pathology Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Pathology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Biochemical Science and Technology College of Life Science National Taiwan University Taipei City 10617 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei City 10617 Taiwan Clinic of General Surgery University Medical Center Rostock Schillingallee 35 Rostock 18057 Germany Clinic of General Surgery Molecular Oncology and Immunotherapy University Medical Center Rostock Schillingallee 69 Rostock 18057 Germany International Ph.D. Program for Cell Therapy and Regeneration Medicine (IPCTRM) School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Post-Baccalaureate Medicine College of Medicine Natioanl Chung Hsing University Taichung Taiwan Cell Therapy and Regenerative Medicine Center and Comprehensive Cancer Center Taichung Veterans General Hospital Taichung Taiwan Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan

Background: Pancreatic ductal adenocarcinoma (PDA) is a pernicious disease characterized by an immunosuppressive milieu that is unresponsive to current immunotherapies. Interleukin-1 receptor antagonist (IL-1Ra) is a natural anti-inflammatory cytokine;however, its contribution to cancer pathogenesis and immunosuppression remains elusive. In this research, we investigated the role and mechanism of IL-1Ra in malignant progression of PDA. Results: Through analyzing clinical dataset and examining the pathological tumor tissues and serum samples, we have demonstrated that IL-1Ra expression is elevated in human PDA and positively associated with malignant progression of PDA. To study the biological function of IL-1Ra in tumors, we generated a set of mouse pancreatic cancer cell lines with a knockout (KO) of the Il1rn gene, encoding IL-1Ra, and compared the tumor growth rates in immune-competent and immune-deficient mice. We found that the Il1rn KO cells exhibited greater tumor inhibition in immune-competent mice, highlighting the crucial role of a functional immune system in Il1rn KO-mediated anti-tumor response. Consistently, we found an increase in CD8⁺ T cells and a decrease in CD11b⁺Ly6G⁻ immunosuppressive mononuclear population in the tumor microenvironment of Il1rn KO-derived tumors. To monitor the inhibitory effects of IL-1Ra on immune cells, we utilized a luciferase-based reporter CD4⁺ T cell line and splenocytes, which were derived from transgenic mice expressing ovalbumin-specific T cell receptors in CD8⁺ T cells, and mice immunized with ovalbumin. We showed that IL-1Ra suppressed T cell receptor signaling and inhibited antigen-specific interferon-γ (IFN-γ) secretion and cytolytic activity in splenocytes. Conclusions: Our findings illustrate the immunosuppressive properties of the natural anti-inflammatory cytokine IL-1Ra, and provide a rationale for considering IL-1Ra-targeted therapies in the treatment of

关键词： IFN-γ Immunosuppression interleukin-1 receptor antagonist Pancreatic adenocarcinoma Patient-derived xenograft Tumor microenvironment

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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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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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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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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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Phase transitions in the frustrated Ising ladder with stoquastic and nonstoquastic catalysts

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Physical Review Research 2021年第4期3卷 043013-043013页

作者： Kabuki Takada Shigetoshi Sota Seiji Yunoki Bibek Pokharel Hidetoshi Nishimori Daniel A. Lidar Department of Physics Tokyo Institute of Technology Yokohama Kanagawa 226-8503 Japan Computational Materials Science Research Team RIKEN Center for Computational Science (R-CCS) Kobe Hyogo 650-0047 Japan Computational Condensed Matter Physics Laboratory RIKEN Cluster for Pioneering Research (CPR) Wako Saitama 351-0198 Japan Computational Quantum Matter Research Team RIKEN Center for Emergent Matter Science (CEMS) Wako Saitama 351-0198 Japan Department of Physics University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA Institute of Innovative Research Tokyo Institute of Technology Yokohama Kanagawa 226-8503 Japan Graduate School of Information Sciences Tohoku University Sendai Miyagi 980-8579 Japan RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) Wako Saitama 351-0198 Japan Department of Electrical & Computer Engineering and Department of Chemistry University of Southern California Los Angeles California 90089 USA

The role of nonstoquasticity in the field of quantum annealing and adiabatic quantum computing is an actively debated topic. We study a strongly-frustrated quasi-one-dimensional quantum Ising model on a two-leg ladder to elucidate how a first-order phase transition with a topological origin is affected by interactions of the ±XX-type. Such interactions are sometimes known as stoquastic (negative sign) and nonstoquastic (positive sign) “catalysts”. Carrying out a symmetry-preserving real-space renormalization group analysis and extensive density-matrix renormalization group computations, we show that the phase diagrams obtained by these two methods are in qualitative agreement with each other and reveal that the first-order quantum phase transition of a topological nature remains stable against the introduction of both XX-type catalysts. This is the first study of the effects of nonstoquasticity on a first-order phase transition between topologically distinct phases. Our results indicate that nonstoquastic catalysts are generally insufficient for removing topological obstacles in quantum annealing and adiabatic quantum computing.

关键词： Adiabatic quantum optimization Phase transitions by order Quantum spin models Density matrix renormalization group Renormalization group

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