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An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images

BMC biomedical engineering 2019年第1期1卷 24页

作者： Hwejin Jung Bilal Lodhi Jaewoo Kang Department of Computer Science and Engineering Korea University Seoul Republic of Korea. Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Republic of Korea.

BACKGROUND:Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding. RESULTS:To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods. CONCLUSIONS:We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. O

关键词： Convolutional neural network Deep learning Histopathology Image analysis Nuclei segmentation

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IoT Solution for Intelligent Pond Monitoring

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IOP Conference Series: Earth and Environmental Science 2020年第1期426卷

作者： Usin Darmalim Ferdinan Darmalim Sutristo Darmalim Alam Ahmad Hidayat Arif Budiarto Bharuno Mahesworo Bens Pardamean Mega Medica Pharmaceuticals Jakarta Indonesia 11840 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Channa striata or the striped snakehead fish is one of snakehead fish species which inhabits all types of freshwater bodies distributed across Asian countries. Because this fish is known to have higher albumin fraction (64.61%) of protein and other economic values, domestication, and cultivation of this fish has been done in many Asian countries such as Indonesia, China, Malaysia, Thailand, Bangladesh, and India. Environmental factors such as temperature, water pH, dissolved oxygen, total dissolved solids, and turbidity are important parameters must be considered inbreeding and growing this type of fish. The aim of this paper is to propose an IoT solution to automatically monitor these environmental factors. It is designed with affordable and open-source electrical components to provide a cost-efficient solution for farmers. Five sensors are used to measure each parameter. A web application prototype is also presented as a companion application for the users to get useful information from the IoT device. It is developed using a Pythonframework. By accessing this web application, the users can immediately detect any abnormal conditions of the pond.

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Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

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Science (New York, N.Y.) 2025年第6734期387卷 eadp4753页

作者： Liu, Hongbo Abedini, Amin Ha, Eunji Ma, Ziyuan Sheng, Xin Dumoulin, Bernhard Qiu, Chengxiang Aranyi, Tamas Li, Shen Dittrich, Nicole Chen, Hua-Chang Tao, Ran Tarng, Der-Cherng Hsieh, Feng-Jen Chen, Shih-Ann Yang, Shun-Fa Lee, Mei-Yueh Kwok, Pui-Yan Wu, Jer-Yuarn Chen, Chien-Hsiun Khan, Atlas Limdi, Nita A. Wei, Wei-Qi Walunas, Theresa L. Karlson, Elizabeth W. Kenny, Eimear E. Luo, Yuan Kottyan, Leah Connolly, John J. Jarvik, Gail P. Weng, Chunhua Shang, Ning Cole, Joanne B. Mercader, Josep M. Mandla, Ravi Majarian, Timothy D. Florez, Jose C. Haas, Mary E. Lotta, Luca A. Drivas, Theodore G. Vy, Ha My T Nadkarni, Girish N. Wiley, Laura K. Wilson, Melissa P. Gignoux, Christopher R. Rasheed, Humaira Thomas, Laurent F. Åsvold, Bjørn Olav Brumpton, Ben M. Hallan, Stein I. Hveem, Kristian Zheng, Jie Hellwege, Jacklyn N. Zawistowski, Matthew Zöllner, Sebastian Franceschini, Nora Hu, Hailong Zhou, Jianfu Kiryluk, Krzysztof Ritchie, Marylyn D. Palmer, Matthew Edwards, Todd L. Voight, Benjamin F. Hung, Adriana M. Susztak, Katalin Department of Medicine Renal Electrolyte and Hypertension Division University of Pennsylvania Philadelphia PA United States Institute of Diabetes Obesity and Metabolism University of Pennsylvania Philadelphia PA United States Department of Genetics University of Pennsylvania Philadelphia PA United States Penn-CHOP Kidney Innovation Center University of Pennsylvania Philadelphia PA United States Liangzhu Laboratory Zhejiang University 1369 West Wenyi Road Hangzhou Zhejiang China Department of Nephrology Children' Hospital Zhejiang University School of Medicine National Clinical Research Center for Child Health Hangzhou Zhejiang China Institute of Molecular Life Sciences HUN-REN Research Center for Natural Sciences Budapest Hungary Department of Molecular Biology Semmelweis University Budapest Hungary Department of Medicine Federal University of São Paulo São Paulo Brazil Department of Biostatistics Vanderbilt University Medical Center Nashville TN United States Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN United States Institute of Clinical Medicine School of Medicine National Yang Ming Chiao Tung University ROC Taipei Taiwan Division of Nephrology Department of Medicine Taipei Veterans General Hospital ROC Taipei Taiwan Institute of Biomedical Sciences Academia Sinica ROC Taipei Taiwan Cardiovascular Center Taichung Veterans General Hospital Taichung Taiwan ROC National Chung Hsing University Taichung Taiwan ROC Heart Rhythm Center Division of Cardiology Department of Medicine Taipei Veterans General Hospital ROC Taipei Taiwan Department of Internal Medicine College of Medicine National Yang Ming Chiao Tung University ROC Taipei Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan ROC Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan ROC Division of Endocrinology and Metabolism Department of Internal Medicine Kaohsiung Medical Universit

Kidney dysfunction is a major cause of mortality, but its genetic architecture remains elusive. In this study, we conducted a multiancestry genome-wide association study in 2.2 million individuals and identified 1026 (97 previously unknown) independent loci. Ancestry-specific analysis indicated an attenuation of newly identified signals on common variants in European ancestry populations and the power of population diversity for further discoveries. We defined genotype effects on allele-specific gene expression and regulatory circuitries in more than 700 human kidneys and 237,000 cells. We found 1363 coding variants disrupting 782 genes, with 601 genes also targeted by regulatory variants and convergence in 161 genes. Integrating 32 types of genetic information, we present the "Kidney Disease Genetic Scorecard" for prioritizing potentially causal genes, cell types, and druggable targets for kidney disease.

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Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex (vol 21, pg 120, 2018)

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NATURE NEUROSCIENCE 2018年第7期21卷 1017-1017页

作者： Hrvatin, Sinisa Hochbaum, Daniel R. Nagy, M. Aurel Cicconet, Marcelo Robertson, Keiramarie Cheadle, Lucas Zilionis, Rapolas Ratner, Alex Borges-Monroy, Rebeca Klein, Allon M. Sabatini, Bernardo L. Greenberg, Michael E. Department of Neurobiology Harvard Medical School Boston MA USA Society of Fellows Harvard University Cambridge MA USA Department of Neurobiology Howard Hughes Medical Institute Harvard Medical School Boston MA USA Image and Data Analysis Core Harvard Medical School Boston MA USA Department of Systems Biology Harvard Medical School Boston MA USA Vilnius University Institute of Biotechnology Vilnius Lithuania ICCB-L Single Cell Core Harvard Medical School Boston MA USA Program for Bioinformatics and Integrative Genomics Graduate School of Arts and Science Division of Medical Sciences Harvard University Cambridge MA USA

Activity-dependent transcriptional responses shape cortical function. However, a comprehensive understanding of the diversity of these responses across the full range of cortical cell types, and how these changes contribute to neuronal plasticity and disease, is lacking. To investigate the breadth of transcriptional changes that occur across cell types in the mouse visual cortex after exposure to light, we applied high-throughput single-cell RNA sequencing. We identified significant and divergent transcriptional responses to stimulation in each of the 30 cell types characterized, thus revealing 611 stimulus-responsive genes. Excitatory pyramidal neurons exhibited inter- and intralaminar heterogeneity in the induction of stimulus-responsive genes. Non-neuronal cells showed clear transcriptional responses that may regulate experience-dependent changes in neurovascular coupling and myelination. Together, these results reveal the dynamic landscape of the stimulus-dependent transcriptional changes occurring across cell types in the visual cortex; these changes are probably critical for cortical function and may be sites of deregulation in developmental brain disorders.

关键词： Gene expression analysis Molecular neuroscience Oligodendrocyte Transcriptomics Visual system

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Erratum for Wang et al., "Clinically Applicable System for Rapidly Predicting Enterococcus faecium Susceptibility to Vancomycin"

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Microbiology spectrum 2022年第1期10卷 e0261021页

作者： Hsin-Yao Wang Chia-Ru Chung Chao-Jung Chen Ko-Pei Lu Yi-Ju Tseng Tzu-Hao Chang Min-Hsien Wu Wan-Ting Huang Ting-Wei Lin Tsui-Ping Liu Tzong-Yi Lee Jorng-Tzong Horng Jang-Jih Lu Department of Laboratory Medicine Chang Gung Memorial Hospitalgrid.413801.f at Linkou Taoyuan City Taiwan. Ph.D. Program in Biomedical Engineering Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Computer Science and Information Engineering National Central Universitygrid.37589.30 Taoyuan City Taiwan. Graduate Institute of Integrated Medicine China Medical Universitygrid.254145.3 Taichung Taiwan. Proteomics Core Laboratory China Medical Universitygrid.254145.3 Hospital Taichung Taiwan. Graduate Program in Biomedical Information Yuan-Ze University Taoyuan City Taiwan. Department of Information Management Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City Taiwan. Clinical Big Data Research Center Taipei Medical University Hospital Taipei City Taiwan. Graduate Institute of Biomedical Engineering Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Division of Haematology/Oncology Department of Internal Medicine Chang Gung Memorial Hospitalgrid.413801.f at Linkou Taoyuan City Taiwan. Biosensor Group Biomedical Engineering Research Center Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Pathology Kaohsiung Chang Gung Memorial Hospitalgrid.413801.f and Chang Gung University College of Medicine Kaohsiung Taiwan. School of Life and Health Sciences The Chinese University of Hong Kong Shenzhen China. Warshel Institute for Computational Biology The Chinese University of Hong Kong Shenzhen China. Department of Bioinformatics and Medical Engineering Asia University Taichung City Taiwan. School of Medicine Chang Gung Universitygrid.145695.a Taoyuan City Taiwan. Department of Medical Biotechnology and Laboratory Science Chang Gung Universitygrid.145695.a Taoyuan City Taiwan.

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Health Assistant Wearable-Based Data Science System Model: A Pilot Study 3

Health Assistant Wearable-Based Data Science System Model: A...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Budiarto, Arif Febriana, Trisna Suparyanto, Teddy Caraka, Rezzy Eko Pardamean, Bens Computer Science Department Bina Nusantara University School of Computer Science Jakarta11480 Indonesia Information Systems Department Bina Nusantara University School of Information Systems Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Indonesia Faculty of Science National University of Malaysia School of Mathematical Science Bangi43000 Malaysia Computer Science Department Bina Nusantara University BINUS Graduate Program-Master of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

The rapid development of Information and Communication Technology (ICT) has an impact on the innovation of new technology that gives benefits for health. One of its latest development is wearable device technology. Nowadays, the growth of this technology market is very positive. But unfortunately the growth of this technology is not followed by the availability of supporting applications that provide maximum benefits for users by utilizing personal health and activity data. This paper is intended to provide a report of our initial research in developing a health assistant solution that utilizes wearable device generated data. The whole research process is executed using Design Thinking approach that put the users as the main consideration. This initial research is started by designing a field research that can potentially gathered users' data generated from wearable device. A series of statistical and machine learning techniques are presented to analyze time series data to create a model. Finally, to implement the model, a mobile application prototype is also proposed. The outcomes from this initial research will be used to develop a fully working solution. © 2018 IEEE.

关键词： Learning systems

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An Investigation of the Required MR Bone Attenuation Correction for Quantitative Whole-Body PET/MR Imaging Using Clinical NaF PET/CT Studies

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International Journal of Medical Physics, Clinical Engineering and Radiation Oncology 2018年第3期7卷 273-295页

作者： Hua A. Ai Osama Mawlawi R. Jason Stafford Jim Bankson Yiping Shao Michele Guindani Richard E. Wendt III Department of Imaging Physics The University of Texas MD Anderson Cancer Center Houston USA Medical Physics Program The University of Texas Graduate School of Biomedical Sciences at Houston Houston USA Department of Radiation Oncology UT Southwest Medical Center Dallas USA Department of Biostatistics The University of Texas MD Anderson Cancer Center Houston USA Biostatistics Bioinformatics and Systems Biology Program The University of Texas Graduate School of Biomedical Sciences at Houston Houston USA

Tissue-classification-based attenuation correction strategies have been previously proposed to correct for bone attenuation in PET/MR imaging and simulated using computed tomography. However, the complication of voxel averaging uniquely associated with bone has not been considered explicitly in the past. This study investigated the effect of voxel averaging between bone and soft tissue in attenuation images and determined how accurately bone must be detected in MR images in order to perform acceptable attenuation correction of PET data by using CT-simulated attenuation correction. We found out that treating bone as soft tissue caused a mean quantification difference of -9.9% ± 5.5% in all 119 bone lesions. There were no significant differences between lesions in the pelvis and the vertebrae. The nominal difference in lesions in the ribs was significantly lower, likely due to the spatial misregistration between the emission and attenuation images. Interestingly, a non-monotonic relationship between the bone imaging ability and the absolute PET quantification accuracy was observed, with the minimal quantification difference achieved at a BVF around 40% for skull lesions (2.6% ± 2.1%), and 30% for non-skull lesions (1.4% ± 1.1%) and all lesions (1.5% ± 1.3%). This study established that a bone classification sensitivity of approximately 30% BVF is required in order for MR-based attenuation correction methods to achieve optimal quantification in whole-body PET/MR studies. For this purpose, higher bone imaging ability of MR may not be necessary.

关键词： PET/MR Attenuation Correction MR-Based Attenuation Correction

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Analyses of protein cores reveal fundamental differences between solution and crystal structures

arXiv

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arXiv 2019年

作者： Mei, Zhe Treado, John D. Grigas, Alex T. Levine, Zachary A. Regan, Lynne O'Hern, Corey S. Integrated Graduate Program in Physical & Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Mechanical Engineering & Materials Science Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Department of Pathology Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

There have been several studies suggesting that protein structures solved by NMR spectroscopy and x-ray crystallography show significant differences. To understand the origin of these differences, we assembled a database of high-quality protein structures solved by both methods. We also find significant differences between NMR and crystal structures|in the root-mean-square deviations of the Cα atomic positions, identities of core amino acids, backbone and sidechain dihedral angles, and packing fraction of core residues. In contrast to prior studies, we identify the physical basis for these differences by modelling protein cores as jammed packings of amino-acid-shaped particles. We find that we can tune the jammed packing fraction by varying the degree of thermalization used to generate the packings. For an athermal protocol, we find that the average jammed packing fraction is identical to that observed in the cores of protein structures solved by x-ray crystallography. In contrast, highly thermalized packing-generation protocols yield jammed packing fractions that are even higher than those observed in NMR structures. These results indicate that thermalized systems can pack more densely than athermal systems, which suggests a physical basis for the structural differences between protein structures solved by NMR and x-ray crystallography. Copyright © 2019, The Authors. All rights reserved.

关键词： X ray crystallography

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Stroke genetics informs drug discovery and risk prediction across ancestries (vol 611, pg 115, 2022)

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NATURE 2022年第7938期612卷 E7-E7页

作者： Mishra, Aniket Malik, Rainer Hachiya, Tsuyoshi Jurgenson, Tuuli Namba, Shinichi Posner, Daniel C. Kamanu, Frederick K. Koido, Masaru Le Grand, Quentin Shi, Mingyang He, Yunye Georgakis, Marios K. Caro, Ilana Krebs, Kristi Liaw, Yi-Ching Vaura, Felix C. Lin, Kuang Winsvold, Bendik Slagsvold Srinivasasainagendra, Vinodh Parodi, Livia Bae, Hee-Joon Chauhan, Ganesh Chong, Michael R. Tomppo, Liisa Akinyemi, Rufus Roshchupkin, Gennady V. Habib, Naomi Jee, Yon Ho Thomassen, Jesper Qvist Abedi, Vida Carcel-Marquez, Jara Nygaard, Marianne Leonard, Hampton L. Yang, Chaojie Yonova-Doing, Ekaterina Knol, Maria J. Lewis, Adam J. Judy, Renae L. Ago, Tetsuro Amouyel, Philippe Armstrong, Nicole D. Bakker, Mark K. Bartz, Traci M. Bennett, David A. Bis, Joshua C. Bordes, Constance Borte, Sigrid Cain, Anael Ridker, Paul M. Cho, Kelly Chen, Zhengming Cruchaga, Carlos Cole, John W. de Jager, Phil L. de Cid, Rafael Endres, Matthias Ferreira, Leslie E. Geerlings, Mirjam I. Gasca, Natalie C. Gudnason, Vilmundur Hata, Jun He, Jing Heath, Alicia K. Ho, Yuk-Lam Havulinna, Aki S. Hopewell, Jemma C. Hyacinth, I. Hyacinth Inouye, Michael Jacob, Mina A. Jeon, Christina E. Jern, Christina Kamouchi, Masahiro Keene, Keith L. Kitazono, Takanari Kittner, Steven J. Konuma, Takahiro Kumar, Amit Lacaze, Paul Launer, Lenore J. Lee, Keon-Joo Lepik, Kaido Li, Jiang Li, Liming Manichaikul, Ani Markus, Hugh S. Marston, Nicholas A. Meitinger, Thomas Mitchell, Braxton D. Montellano, Felipe A. Morisaki, Takayuki Mosley, Thomas H. Nalls, Mike A. Nordestgaard, Borge G. O'Donnell, Martin J. Okada, Yukinori Onland-Moret, N. Charlotte Ovbiagele, Bruce Peters, Annette Psaty, Bruce M. Rich, Stephen S. Rosand, Jonathan Sabatine, Marc S. Sacco, Ralph L. Saleheen, Danish Sandset, Else Charlotte Salomaa, Veikko Sargurupremraj, Muralidharan Sasaki, Makoto Satizabal, Claudia L. Schmidt, Carsten O. Shimizu, Atsushi Smith, Nicholas L. Sloane, Kelly L. Sutoh, Yoichi Sun, Yan V. Tanno, Kozo Tiedt, Steffen Tatlisumak, Turgut Torres-Aguila, Nuria P. Tiwari, Hemant K. Tregouet, Bordeaux Population Health Research Center University of Bordeaux Inserm UMR 1219 Bordeaux France Department of Public Health Bordeaux University Hospital Bordeaux France Department of Neurology Memory Clinic Bordeaux University Hospital Bordeaux France Department of Neurology Institute for Neurodegenerative Diseases CHU de Bordeaux Bordeaux France Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Munich Germany Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of Harvard and the Massachusetts Institute of Technology Cambridge MA USA Munich Cluster for Systems Neurology Munich Germany German Center for Neurodegenerative Diseases (DZNE) Munich Germany Iwate Tohoku Medical Megabank Organization Iwate Medical University Iwate Japan Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia Institute of Mathematics and Statistics University of Tartu Tartu Estonia Department of Computational Biology University of Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland University Center for Primary Care and Public Health Lausanne Switzerland Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Department of Genome Informatics Graduate School of Medicine The University of Tokyo Tokyo Japan Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan Integrated Frontier Research for Medical Science Division Institute for Open and Transdisciplinary Research Initiatives Osaka University Suita Japan Center for Infectious Disease Education and Research (CiDER) Osaka University Suita Japan Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA TIMI S

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Jammed packings of 3D superellipsoids with tunable packing fraction, contact number, and ordering

arXiv

引用

arXiv 2019年

作者： Yuan, Ye VanderWerf, Kyle Shattuck, Mark D. O’Hern, Corey S. Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department City College of New York New YorkNY10031 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

We carry out numerical studies of static packings of frictionless superellipsoidal particles in three spatial dimensions. We consider more than 200 different particle shapes by varying the three shape parameters that define superellipsoids. We characterize the structural and mechanical properties of both disordered and ordered packings using two packing-generation protocols. We perform athermal quasi-static compression simulations starting from either random, dilute configurations (Protocol 1) or thermalized, dense configurations (protocol 2), which allows us to tune the orientational order of the packings. In general, we find that the contact numbers at jamming onset for superellipsoid packings are hypostatic, with zJ f and df = 5 or 6 depending on whether the particles are axi-symmetric or not. Over the full range of orientational order, we find that the number of quartic modes of the dynamical matrix for the packings always matches the number of missing contacts relative to the isostatic value. This result suggests that there are no mechanically redundant contacts for ordered, yet hypostatic packings of superellipsoidal particles. Additionally, we find that the packing fraction at jamming onset for diordered packings of superellipsoidal particles can be collapsed using two particle shape parameters, e.g. the asphericity A and reduced aspect ratio β of the particles. Copyright © 2019, The Authors. All rights reserved.

关键词： Jamming

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