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arXiv 2024年

作者： Moreno, Matthew Andres Zaman, Luis Dolson, Emily Department of Ecology and Evolutionary Biology Ann Arbor United States Center for the Study of Complex Systems Ann Arbor United States Michigan Institute for Data and AI in Society Ann Arbor United States University of Michigan Ann Arbor United States Department of Computer Science and Engineering East Lansing United States Program in Ecology Evolution and Behavior East Lansing United States Michigan State University East Lansing United States

Operations over data streams typically hinge on efficient mechanisms to aggregate or summarize history on a rolling basis. For high-volume data steams, it is critical to manage state in a manner that is fast and memory efficient - particularly in resource-constrained or real-time contexts. Here, we address the problem of extracting a fixed-capacity, rolling subsample from a data stream. Specifically, we explore "data stream curation" strategies to fulfill requirements on the composition of sample time points retained. Our "DStream" suite of algorithms targets three temporal coverage criteria: (1) steady coverage, where retained samples should spread evenly across elapsed data stream history;(2) stretched coverage, where early data items should be proportionally favored;and (3) tilted coverage, where recent data items should be proportionally favored. For each algorithm, we prove worst-case bounds on rolling coverage quality. In contrast to previous work by Moreno, Rodriguez Papa, and Dolson (2024), which dynamically scales memory use to guarantee a specified level of coverage quality, here we focus on the more practical, application-driven case of maximizing coverage quality given a fixed memory capacity. As a core simplifying assumption, we restrict algorithm design to a single update operation: writing from the data stream to a calculated buffer site - with data never being read back, no metadata stored (e.g., sample timestamps), and data eviction occurring only implicitly via overwrite. Drawing only on primitive, low-level operations and ensuring full, overhead-free use of available memory, this "DStream" framework ideally suits domains that are resource-constrained (e.g., embedded systems), performance-critical (e.g., real-time), and fine-grained (e.g., individual data items as small as single bits or bytes). In particular, proposed power-of-two-based buffer layout schemes support O(1) data ingestion via concise bit-level operations. To further practical applica

关键词： data streams

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Comparison of multivariate adaptive Poisson Regression spline and multivariate adaptive generalized Poisson Regression spline

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AIP Conference Proceedings 2023年第1期2536卷

作者： Septia Devi Prihastuti Yasmirullah Bambang Widjanarko Otok Jerry Dwi Trijoyo Purnomo Dedy Dwi Prastyo Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia

The multivariate adaptive regression (MARS) spline is a statistical method introduced by Friedman in 1991. The MARS method has several advantages, especially for data that consider the potential nonlinear pattern. These produce a continuous model on knots where the knot selection process is performed via adaptive procedures, capturing additive effects and interactions between predictor variables, and applied to data modeling with numerical and categorical responses. However, the analyzed data is not only numerical or categorical data, but also count type data. Count type data is often found in the health sector, for example, the number of data on tuberculosis. It invites to modify the MARS model with Poisson regression and generalized Poisson regression, i.e. Multivariate Adaptive Poisson Regression Spline (MAPRS) and Multivariate Adaptive Generalized Poisson Regression Spline (MAGPRS) . In addition, this study aimed to compare MAPRS and MAGPRS when applied to tuberculosis data. By applying MAPRS and MAGPRS to model the number of tuberculosis in Lamongan regency, the MAPRS method outperforms the MAGPRS method. The MAPRS method has a lower RMSE value than the MAGPRS method.

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Structural study of CaO-based materials with ZrO2 incorporation using in-situ synchrotron radiation X-ray diffraction and X-ray absorption spectroscopy

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Ceramics International 2025年

作者： Munawaroh, Fatimatul Masdya, Yoga Saiyasombat, Chatree Baqiya, Malik A. Triwikantoro Physics Department Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Natural Science Education Study Program Universitas Trunojoyo Madura Bangkalan69162 Indonesia Nakhon Ratchasima30000 Thailand

Pure CaO powder (CZ0) and CaO incorporated with 10 wt% ZrO2 (CZ10) have been successfully synthesized from natural limestone using the sol-gel and wet-mixing methods, respectively. Phase and structural stability were examined using in-situ Synchrotron Radiation-XRD, X-ray Absorption Near Edge Structure (XANES), and Extended X-ray Absorption Fine Structure (EXAFS) analyses. The result indicates that the calcium (Ca) atom is slightly distorted by ZrO2 incorporation, and ZrO2 increases the Ca-O bond distance (2.4014 Å) in the CaO-ZrO2. Carbonation experiments demonstrate that CZ10 achieves a higher formation of CaCO3 (⁓81.63 ± 1.99 wt%) compared to CZ0 (⁓64.50 ± 1.46 wt%). The carbonation process results in Ca-O bond elongation to 2.4142 Å, indicating transition to carbonate phases and an altered calcium electronic environment. No significant reduction in the Ca-O bond length (2.3977 Å) occurs after decarbonization, suggesting that the removal of CaCO3 from the CaO structure can be stabilized by Zr incorporation. Therefore, this material holds great potential for application in CO2 capture. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： X ray absorption near edge structure spectroscopy

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Survival analysis of Kasturi tobacco plants in Jember district using the stratified cox and extended cox models.

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MethodsX 2025年 14卷

作者： Pratama, Angga Iryanto Rantini, Dwi Ningrum, Ratih Ardiati Ramadan, Arip Othman, Fazidah Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Research Group of Data-Driven Decision Support System Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaya Malaysia

In 2022, tobacco was the top export commodity, generating $106.3 million in sales. East Java Province contributes significantly to Indonesia's tobacco production, with an annual output of 188.6 thousand tons. Jember Regency is the area in East Java Province which is a Kasturi tobacco cultivation area. The productivity of Kasturi tobacco continues to decrease due to several factors that can affect the survival of the tobacco. This study aims to analyze the survival of Kasturi tobacco by creating stratified Cox and extended Cox models in order to handle factors that cannot fulfill the proportional hazard assumption. Based on the results of the analysis, the stratified Cox model is the best model with AIC and BIC values of 798.108 and 805.5748 respectively, while the extended Cox has AIC and BIC values of 840.2186 and 850.1732 respectively. Variables that can significantly affect the survival of Kasturi tobacco are the variable concentration of ZA fertilizer, pesticide concentration. The addition of ZA fertilizer must be appropriate, because if excessive it can cause poisoning of tobacco plants. Likewise with pesticides, if excessive it will cause damage to the leaves. This policy can increase the productivity of Kasturi tobacco. Then, Jember Regency contributes greater export capacity. • This paper aims to determine the factors that affect the survival of Kasturi tobacco plants in Jember • By using the extended Cox model, the best one is obtained using the Heaviside function with AIC and BIC values of 840.2186 and 850.1732, respectively • The stratified Cox model is better than the extended Cox model, which has AIC and BIC values of 798.108 and 805.5748, respectively © 2025 The Author(s)

关键词： Cox proportional hazard Extended cox Kasturi tobacco Productive agriculture Stratified cox

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Integrin αEβ7+ T cells direct intestinal stem cell fate decisions via adhesion signaling

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细胞研究（英文版） 2021年第12期31卷 1291-1307页

作者： Shiyang Chen Yajuan Zheng Xiaojuan Ran Hui Du Hua Feng Lei Yang Yating Wen Changdong Lin Shihui Wang Mengwen Huang Zhanjun Yan Dianqing Wu Hongyan Wang Gaoxiang Ge An Zeng Yi Arial Zeng Jianfeng Chen School of Life Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhouZhejiangChina State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyChinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghaiChina State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell ScienceShanghai Institute of Biochemistry and Cell BiologyChinese Academy of SciencesUniversity of Chinese Academy of SciencesShanghaiChina Omics Core Bio-Med Big Data CenterShanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghaiChina Department of Orthopedics Suzhou Ninth Peoples HospitalSoochow UniversitySuzhouJiangsuChina Department of Pharmacology Vascular Biology and Therapeutic ProgramYale School of MedicineNew HavenCTUSA

Intestinal stem cell (ISC) differentiation is regulated precisely by a niche in the crypt,where lymphocytes may interact with stem and transient amplifying (TA) ***,whether and how lymphocyte-stem/TA cell contact affects ISC differentiation is largely ***,we uncover a novel role of T cell-stem/TA cell contact in ISC fate *** show that intestinal lymphocyte depletion results in skewed ISC differentiation in mice,which can be rescued by T cell ***,integrin αEβ7 expressed on T cells binds to E-cadherin on ISCs and TA cells,triggering E-cadherin endocytosis and the consequent Wnt and Notch signaling *** αEβ7-E-cadherin adhesion suppresses Wnt signaling and promotes Notch signaling in ISCs and TA cells,leading to defective ISC ***,αEβ7+ T cells regulate ISC differentiation at single-cell level through cell-cell contact-mediated αEβ7-E-cadherin adhesion signaling,highlighting a critical role of the T cell-stem/TA cell contact in maintaining intestinal homeostasis.

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Empowering Biomedical Discovery with AI Agents

arXiv

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arXiv 2024年

作者： Gao, Shanghua Fang, Ada Huang, Yepeng Giunchiglia, Valentina Noori, Ayush Schwarz, Jonathan Richard Ektefaie, Yasha Kondic, Jovana Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Chemistry and Chemical Biology Harvard University CambridgeMA United States Program in Biological and Biomedical Sciences Harvard Medical School BostonMA United States Department of Brain Sciences Imperial College London London United Kingdom Harvard College CambridgeMA United States Program in Biomedical Informatics Harvard Medical School BostonMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University MA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative CambridgeMA United States

We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI’s ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies. © 2024, CC BY.

关键词： Adversarial machine learning

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State-transition dynamics of resting-state functional magnetic resonance imaging data: Model comparison and test-to-retest analysis

arXiv

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arXiv 2023年

作者： Islam, Saiful Khanra, Pitambar Nakuci, Johan Muldoon, Sarah F. Watanabe, Takamitsu Masuda, Naoki Department of Mathematics University at Buffalo State University of New York Buffalo United States Institute for Artificial Intelligence and Data Science University at Buffalo State University of New York Buffalo United States Neuroscience Program University at Buffalo State University of New York Buffalo United States School of Psychology Georgia Institute of Technology United States International Research Centre for Neurointelligence The University of Tokyo Institutes for Advanced Study Tokyo Japan

Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of *** Codes 92F05 © 2023, CC BY.

关键词： Dynamics

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On the computational Bayesian survival spatial Dengue hemorrhagic fever (DHF) modelling with Fernandez–steel skew normal conditional autoregressive (FSSN CAR) frailty

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AIP Conference Proceedings 2023年第1期2536卷

作者： Dwi Rantini Nur Iriawan Irhamah Musofa Rusli Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia Department of Internal Medicine Faculty of Medicine Universitas Airlangga Surabaya 60115 Indonesia

Generally, infectious disease data has a spatial effect, meaning that areas that are close together affect each other. One of the infectious diseases is dengue hemorrhagic fever (DHF). The recovery time of DHF patients is interesting to study. In this research, DHF data in eastern Surabaya was used. The recovery time of this DHF patient can be modeled using one of the statistical methods, namely survival analysis. This recovery time data follows the Weibull distribution. On the data indicated spatial effect, the conditional autoregressive (CAR) model can be used to express dependencies between adjacent areas. Spatial random effects in the survival model were modeled with Normal CAR, Double-Exponential (DE) CAR, and Fernandez-Steel skew Normal (FSSN) CAR. In this research, Cox regression was used and parameter estimation was performed using the Bayesian analysis with Hamiltonian Monte Carlo (HMC) algorithm using the Stan programming language. Based on the comparison of the Watanabe-Akaike information criterion (WAIC), the spatial random effects on the Weibull Cox regression model are best modeled with the FSSN CAR. This is because the FSSN CAR is able to capture error patterns both symmetrical and asymmetrical, not with Normal CAR and DE CAR which can only capture symmetrical error patterns. In this research, several variables that allegedly affect the recovery rate of DHF patients are given. Then, based on the best model, variables that significantly affect the patient's recovery rate are age, the high schools in last education, housewife in the type of occupation, stadium-II in severity level, fever days before entering the hospital, pulse, temperature, and leukocytes.

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Implementation of online learning during the COVID-19 pandemic in primary schools

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. Anugrahana E. Liswantiani D. Sudigyo T. Suparyanto B. Pardamean Elementary School Teacher Education Study Program (PGSD) Faculty of Teacher Training and Education Sanata Dharma University Yogyakarta Indonesia 55281 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The COVID-19 pandemic requires face-to-face learning to shift to online or distance learning. Therefore, the elementary school, which is the most basic level of education affected by this phenomenon, where this research is carried out. This study aimed to analyze the process of implementing online learning, the supporting and inhibiting factors for teachers, students, and parents in implementing online learning during the COVID-19 pandemic. In addition, good practices for online learning were also analyzed in this study to understand these phenomena in elementary schools. The research method utilized descriptive qualitative research and was performed at a private primary school in Yogyakarta. The subjects consisted of teachers, students, and parents that were involved in the online learning process. Online questionnaires and interviews were used to collect data. The results of this study show the enormous impact of the COVID-19 pandemic on the learning process. Learning that is usually carried out face-to-face (conventional) has now been converted into online learning in elementary school.

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Fabrication a sustainable adsorbent nanocellulose-mesoporous hectorite bead for methylene blue adsorption

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Case Studies in Chemical and Environmental Engineering 2024年 10卷

作者： Holilah Asranudin El Messaoudi, Noureddine Ulfa, Maria Hamzah, Amir Hamid, Zuratul Ain Abdul Ramadhani, Dini Viandi Suryanegara, Lisman Mahardika, Melbi Melenia, Alvina Tata Pratama, Agus Wedi Prasetyoko, Didik Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Research Collaboration Center for Nanocellulose BRIN-Andalas University Research Collaboration Center for Biomass and Biorefinery Between BRIN and Universitas Padjadjaran Laboratory of Applied Chemistry and Environment Faculty of Sciences Ibn Zohr University Chemistry Education Study Program Faculty of Teacher Training and Education Sebelas Maret University Deputy for Infrastructure Research and Innovation National Research and Innovation Agency of Indonesia (BRIN) School of Materials & Mineral Resources Engineering Universiti Sains Malaysia Department of Biology Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember (ITS) Kampus ITS Sukolilo

In this study, fabrication of mesoporous nanocellulose-hectorite-alginate bead (B-NcH) composites and their application for adsorption of MB from water was carried out. The synthesis of nanocellulose (NC) was conducted from mangosteen waste peel (Garcinia mangostana L.). Garcinia mangostana L. peel had 58.9 ± 0,41 % content of cellulose. NCs was successfully isolated using the acid hydrolysis method. NC from Garcinia mangostana L. peel had a diameter of 24.5 ± 0.2 nm and a length of 381.2 ± 10.4 nm. The superabsorbent was composed of nanocrystalline cellulose, mesoporous hectorite, and alginate. The functional group, phase, and morphology of composite was investigated. The composite with 5 % NC had excellent mechanical strength. The B-NcH5 showed a high adsorption capacity of methylene blue (MB), reaching 98.64 % at an adsorbent dose of 30 mg for 90 min. The presence of hectorite can increase the adsorption ability compared to NCs-alginate. The equilibrium adsorption data followed the Freundlich isotherm;however, the kinetics of adsorption showed the usefulness of adopting the PSO kinetic model. The highest adsorption capacity of B-NcH5 for MB was determined to be 57.59 mg/g. For thermodynamic studies, it was revealed that the adsorption of MB on BNcH-5 is a spontaneous and endothermic process. The removal efficiency was observed in six consecutive cycles, indicating at the favorable reusability of this adsorbent. These results validate the potential use of this B-NcH5 adsorbent for water treatment applications. © 2024

关键词： Adsorption Bead Hectorite Methylene blue Nanocellulose Water treatment

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