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The nanotoxicity assessment of cube-like iron nitride magnetic nanoparticles at the organismal level of nematodeCaenorhabditis elegans

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Nanotoxicology 2022年第4期16卷 472-483页

作者： Greici Gubert Priscila Gubert Jana Messias Sandes Julia Bornhorst Luiz Carlos Alves Caroline Brandão Quines Dante Homero Mosca a Departamento de Física Universidade Federal do Paraná Curitiba Brazilb Instituto Federal Catarinense Rio do Sul Brazil c Graduate Program in Pure and Applied Chemistry POSQUIPA. Federal University of Western Bahia Barreiras Brazild Immunopathology Laboratory Keizo Asami LIKA. Federal University of Pernambuco Recife Brazil d Immunopathology Laboratory Keizo Asami LIKA. Federal University of Pernambuco Recife Brazil e Department of Food Chemistry Institute of Nutritional Science University of Potsdam Nuthetal Germanyf Food Chemistry Faculty of Mathematics and Natural Sciences University of Wuppertal Wuppertal Germany g Oswaldo Cruz Foundation Aggeu Magalhães Institute Department of Virology and Experimental Therapy Recife Brazil h Postgraduating Program in Biochemistry Federal University of Pampa (UNIPAMPA) Uruguaiana Brazil a Departamento de Física Universidade Federal do Paraná Curitiba Brazil

Magnetic nanoparticles (NPs) are suitable candidates for various medical and biological applications, despite some concerns that they may have negative impacts on human health. In this study, the toxicity effects of magnetic NPs consisting of α”-Fe16N2captured and bioaccumulated by the nematodeCaenorhabditis elegans(C. elegans) in the early larval stage are evaluated. The choice of α”-Fe16N2NPs is based on their good structural stability when stored in saline solution and high magnetic performance. The uptake and bioaccumulation of α”-Fe16N2NPs in intestinal cells ofC. eleganswas evidenced by transmission electron microscopy. After exposure to NPs up to 40 mg mL−1,C. eleganslarval development, survival, feeding behavior, defecation cycles, movement and reproduction were monitored.C. eleganssurvival and other monitored behavioral evolutions do not show significant changes, except for a slight statistical reduction in the reproductive profile. Therefore, the present results are promising and very encouraging for investigations of applications of α”-Fe16N2NPs in the biomedical area.

关键词： Nanotoxicology C. elegans Nanoparticles α”-Fe16N2 iron nitride

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Cellular organization in lab-evolved and extant multicellular species obeys a maximum entropy law

arXiv

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arXiv 2021年

作者： Day, Thomas C. Höhn, Stephanie S. Zamani-Dahaj, Seyed A. Yanni, David Burnetti, Anthony Pentz, Jennifer Honerkamp-Smith, Aurelia R. Wioland, Hugo Sleath, Hannah R. Ratcliff, William C. Goldstein, Raymond E. Yunker, Peter J. School of Physics Georgia Institute of Technology AtlantaGA United States Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge United Kingdom School of Biological Sciences Georgia Institute of Technology AtlantaGA United States Quantitative Biosciences Graduate Program Georgia Institute of Technology AtlantaGA United States Department of Molecular Biology Umeå University Umeå Sweden

The prevalence of multicellular organisms is due in part to their ability to form complex structures. How cells pack in these structures is a fundamental biophysical issue, underlying their functional properties. However, much remains unknown about how cell packing geometries arise, and how they are affected by random noise during growth - especially absent developmental programs. Here, we quantify the statistics of cellular neighborhoods of two different multicellular eukaryotes: lab-evolved "snowflake" yeast and the green alga Volvox carteri. We find that despite large differences in cellular organization, the free space associated with individual cells in both organisms closely fits a modified gamma distribution, consistent with maximum entropy predictions originally developed for granular materials. This 'entropic' cellular packing ensures a degree of predictability despite noise, facilitating parent-offspring fidelity even in the absence of developmental regulation. Together with simulations of diverse growth morphologies, these results suggest that gamma-distributed cell neighborhood sizes are a general feature of multicellularity, arising from conserved statistics of cellular packing. © 2021, CC BY.

关键词： Cytology

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Probabilistic connectivity of random graphs and their unions

arXiv

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arXiv 2019年

作者： Bjorkman, Beth Hale, Matthew Lamkin, Thomas Robinson, Benjamin Thompson, Craig Iowa State University Department of Mathematics Department of Mechanical and Aerospace Engineering University of Florida Air Force Research Laboratory Sensors Directorate University of Arizona Program in Applied Mathematics

Graph-theoretic methods have seen wide use throughout the literature on multi-agent control and optimization. When communications are intermittent and unpredictable, such networks have been modeled using random communication graphs. When graphs are time-varying, it is common to assume that their unions are connected over time, yet, to the best of our knowledge, there are not results that determine the number of random graphs needed to attain a connected union. Therefore, this paper bounds the probability that individual random graphs are connected and bounds the same probability for connectedness of unions of random graphs. The random graph model used is a generalization of the classic Erdos-R ényi model which allows some edges never to appear. Numerical results are presented to illustrate the analytical developments made. Copyright © 2019, The Authors. All rights reserved.

关键词： Graph theory

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A general semi-parametric elliptical distribution model for semi-supervised learning

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Journal of Nonparametric Statistics 2025年第2期37卷

作者： Chin-Tsang Chiang Sheng-Hsin Fan Ming-Yueh Huang Jen-Chieh Teng Alvin Lim a Institute of Applied Mathematical Sciences National Taiwan University Taipei Taiwan b Department of Mathematics National Taiwan University Taipei Taiwan c Institute of Statistical Science Academia Sinica Taipei Taiwan d Data Science Degree Program National Taiwan University Taipei Taiwan e Data Science Department Measured Inc. Austin TX USAf Goizueta Business School Emory University Atlanta GA USA

ABSTRACTThis research proposes a novel semi-parametric elliptical distribution model for application in semi-supervised learning tasks. We use labelled and unlabelled data to develop a pseudo maximum likelihood method for estimation and classification. The proposed estimator outperforms the estimator based solely on labelled data and achieves the semi-parametric efficiency bound with a suitable size of unlabelled data. We efficiently maximise the objective function by utilising low-dimensional groupwise pseudo-likelihood functions in a block coordinate descent manner while ensuring numerical stability and convergence through appropriate bandwidth selectors and initial parameter estimates. Additionally, the study comprehensively investigates the impact of labelled and unlabelled data on the pseudo maximum likelihood estimator and classifier. Simulation studies and empirical data applications illustrate the superiority of our methodology.

关键词： elliptical distribution posterior group probability pseudo maximum likelihood method semi-parametric efficiency semi-supervised learning

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Differential gene expression in trabecular bone osteocytes is related to the local strain and strain gradient

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Scientific reports 2025年第1期15卷 18501页

作者： Meghana Machireddy Sarah Nano Lucas Debiase Jonathan Lewis Sara Cole Jun Li Glen L Niebur University of Notre Dame Bioengineering Graduate Program Notre Dame IN USA. Department of Aerospace and Mechanical Engineering Notre Dame IN USA. Notre Dame Integrated Imaging Facility Notre Dame IN USA. Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre Dame IN USA. University of Notre Dame Bioengineering Graduate Program Notre Dame IN USA. gniebur@nd.edu. Department of Aerospace and Mechanical Engineering Notre Dame IN USA. gniebur@nd.edu.

Osteocytes regulate the response of osteoclasts and osteoblasts to mechanical loading through signaling molecules, the levels of which are controlled by post-translational modification or degradation and by differential gene transcription and translation. The magnitude and mode of bone tissue deformation that elicits a transcriptional response in individual osteocytes in situ has been difficult to quantify. We measured SOST, Wnt11, TNF, and FRZB gene expression in osteocytes within loaded and unloaded control porcine trabecular bone explants using RNAScope® and compared the local tissue level strain and strain gradient-which we used as an indicator of potential poroelastic fluid flow-in the tissue surrounding osteocytes with high vs. low gene expression. The measured expression of all four genes differed between loaded and unloaded explants, on average, with the mean SOST expression level decreasing by 45%. In the loaded explants, gene expression was altered from baseline in about 30% of the osteocytes, and they were surrounded by tissue with higher strain and strain gradient than the 20 to 25% of osteocytes that remained near baseline expression. Both deviatoric strain and hydrostatic strain gradient were sensitive and specific predictors of the mechanobiological response for individual genes as well as combinations. SOST expression was highly related to elevated strain gradient, providing evidence that osteocytes respond to fluid flow in the lacunar-canalicular system.

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Dosimetric analysis of beam number variations in IMRT for head-and-neck, breast, and pelvic cancers using Halcyon

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Radiation Physics and Chemistry 2025年 236卷

作者： Phat, Luong Tien Thao, Mai Thi Kien, Tran Trung Tai, Duong Thanh Sandwall, Peter Sulieman, Abdelmoneim Tamam, Nissren Yani, Sitti Chow, James C.L. Department of Radiation Oncology University Medical Shing Mark Hospital Viet Nam Department of Radiation Oncology Phuc Thinh General Hospital Viet Nam Department of Medical Physics Faculty of Medicine Nguyen Tat Thanh University Viet Nam Department of Radiation Oncology OhioHealth Mansfield OH United States Department of Radiological Sciences College of Applied Medical Sciences King Saud Bin Abdulaziz University for Health Sciences Al Ahsa Saudi Arabia Imaging Physics Section Research and Innovation Department King Faisal Specialist Hospital & Research Center Riyadh Saudi Arabia Department of Physics College of Science Princess Nourah bint Abdulrahman University P.O Box 84428 Riyadh11671 Saudi Arabia Department of Physics Faculty of Mathematics and Natural Sciences IPB University Jalan Meranti Kampus IPB Dramaga Bogor16680 Indonesia Department of Radiation Oncology University of Toronto Toronto Canada Radiation Medicine Program Princess Margaret Cancer Centre University Health Network Toronto Canada

This study investigated the dosimetric effects of varying beam numbers (9, 11, and 13) in intensity-modulated radiation therapy (IMRT) using the Halcyon linear accelerator at ShingMark University Hospital. IMRT plans for 27 patients with head-and-neck, breast, and pelvic cancers were evaluated according to the RTOG 0615 protocol, focusing on Planning Target Volume (PTV) dose coverage, uniformity, maximum and minimum doses, and Organ-at-Risk (OAR) dose constraints. For breast cancer, the 11-beam plan improved PTV coverage (V50Gy: 97.9 ± 0.5 %) and reduced maximum dose compared to 9- and 13-beam plans, while the 13-beam plan lowered heart mean dose by up to 10.5 % in left breast cases. In pelvic cases, the 13-beam plan enhanced OAR sparing, reducing bowel V35Gy by 10.5 % compared to the 9-beam plan, with minimal PTV coverage differences ( © 2025 The Authors

关键词： Diseases

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Two-Stage Maximum Score Estimator

arXiv

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arXiv 2020年

作者： Gao, Wayne Yuan Xu, Sheng Xu, Kan Department of Economics University of Pennsylvania 133 S 36th St. PhiladelphiaPA19104 United States The Program in Applied and Computational Mathematics Princeton University Fine Hall Washington Road PrincetonNJ08544 United States

This paper considers the asymptotic theory of a semiparametric M-estimator that is generally applicable to models that satisfy a monotonicity condition in one or several parametric indexes. We call this estimator the two-stage maximum score (TSMS) estimator, since our estimator involves a first-stage nonparametric regression when applied to the binary choice model of Manski (1975, 1985). We characterize the asymptotic distribution of the TSMS estimator, which features phase transitions depending on the dimension of the first-stage estimation. Effectively, the first-stage nonparametric estimator serves as an imperfect smoothing function on a non-smooth criterion function, leading to the pivotality of the first-stage estimation error with respect to the second-stage convergence rate and asymptotic distribution. Copyright © 2020, The Authors. All rights reserved.

关键词： Functions

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Fuzzy sentiment analysis using convolutional neural network

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AIP Conference Proceedings 2021年第1期2329卷

作者： Sugiyarto Joko Eliyanto Nursyiva Irsalinda Meita Fitrianawati 1Mathematics Department Faculty of Applied Science and Technology Universitas Ahmad Dahlan Indonesia 2Mathematics Education Department Postgraduate Program Universitas Ahmad Dahlan Indonesia 3Elementary School Teacher Study Program Faculty of Teacher Training and Education Universitas Ahmad Dahlan Indonesia

Sentiment analysis is one part of natural language processing. Sentiment analysis can be done by lexicon based, or machine learning based. Sentiment analysis based on machine learning has advantage of dynamism to meet with new language datasets or new vocabulary. Sentiment analysis seeks to understand the sentiments contained in a sentence. A sentence can be positive, neutral or negative, based on its sentiments. A sentence can have positive, neutral or negative sentiments. However, the fact is each sentence does not always have positive, negative or neutral sentiment clearly. We try to develop a sentiment analysis method that can show the sentiment degree of a sentence. Fuzzy sentiment analysis using convolutional neural network are introduced in this paper to produce more accurate sentiment analysis results. Convolutional neural networks are a popular machine learning method for sentiment analysis. The concept of fuzzy sets is used to express the sentiment degree of a sentence. Euclidean distance analysis to determine the proximity of two vectors is used to show that this method is better than the standard method. The method we propose successfully produces a value that indicates the degree of sentiment of a sentence. Comparison of the euclid distance between the results of the standard sentiment analysis and our method shows that the results of the fuzzy sentiment analysis using convolutional neural network have a distance that is relatively close to the true sentiment value. Fuzzy convolutional neural network analysis sentiment is proven to be able to produce better and smoother sentiment analysis results than standard methods.

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Modelling and forecasting daily average temperature in Bangkok, Thailand

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AIP Conference Proceedings 2024年第1期3123卷

作者： Kanyarat Thitiwatthanakan Manad Khamkong Ravi Lonkani Thanasak Mouktonglang Ph.D. Program in Applied Statistics Department of Statistics Faculty of Science Chiang Mai University 50200 Chiang Mai Thailand Department of Statistics Faculty of Science Chiang Mai University 50200 Chiang Mai Thailand Department of Finance Faculty of Business Administration Chiang Mai University 50200 Chiang Mai Thailand Department of Mathematics Faculty of Science Chiang Mai University 50200 Chiang Mai Thailand

In order to predict the occurrence of extreme climate events, forecasting and describing dynamic changes in temperature are essential. Understanding these events will allow action to be taken in order to minimize their associated effects. In this paper, we present a feasible model for estimating daily average temperatures in Bangkok, Thailand. The daily maximum and minimum temperature observations between 2006 and 2021 were collected from the Thai Meteorological Department for the study. The Fourier series was then applied to the average daily temperature as the mean function, and then the auto-regressive integrated moving average, ARIMA (4,1,1), was used to predict the residuals that occurred from the mean function to describe the evolution of the temperature. Analysis of the root mean square error (RMSE) value from the models, which is 0.9536, revealed that the methods fitted the data quite well.

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Correction to: Finite dimensional state representation of physiologically structured populations

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Journal of mathematical biology 2020年第3期81卷 905-906页

作者： Odo Diekmann Mats Gyllenberg Johan A J Metz Department of Mathematics University of Utrecht P.O. Box 80010 3508 TA Utrecht The Netherlands. Department of Mathematics and Statistics University of Helsinki P.O. Box 68 00014 Helsinki Finland. mats.gyllenberg@helsinki.fi. Mathematical Institute and Institute of Biology Leiden University 2333 CA Leiden The Netherlands. Evolution and Ecology Program International Institute of Applied Systems Analysis 2361 Laxenburg Austria.

In the original publication of the article, the Subsection 2.1.2 was published incorrectly.

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