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Chronic radiation-associated dysphagia in oropharyngeal cancer survivors: Towards age-adjusted dose constraints for deglutitive muscles

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CLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY 2019年 18卷 16-22页

作者： Christopherson, Kaitlin M. Ghosh, Alokananda Sherif, Abdallah Mohamed, Radwan Kamal, Mona Fuller, David Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston TX United States Department of Biostatistics and Data Science University of Texas School of Public Health Houston TX United States Department of Clinical Oncology University of Alexandria Alexandria Egypt Department of Emergency Medicine The University of Texas MD Anderson Cancer Center Houston TX United States Baylor College of Medicine Houston TX United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA United States Department of Radiation Oncology The University of Texas Medical Branch Galveston TX United States Department of Head and Neck Surgery The University of Texas MD Anderson Cancer Center Houston TX United States Department of Thoracic & Head and Neck Oncology The University of Texas MD Anderson Cancer Center Houston TX United States Medical Physics Program The University of Texas Graduate School of Biomedical Sciences Houston TX United States Department of Computer Science University of Illinois at Chicago Chicago IL United States Department of Electrical and Computer Engineering The University of Iowa Iowa City IA United States

Objectives: We sought to model chronic radiation-associated dysphagia (RAD) in patients given intensity-modulated radiation therapy (IMRT) for oropharyngeal squamous cell cancer (OPSCC) as a function of age and dose to non-target swallowing muscles. Methods: We reviewed 300 patients with T1-T4 N0-3 M0 OPSCC given definitive IMRT with concurrent chemotherapy. Chronic RAD was defined as aspiration or stricture on videoflouroscopy/endoscopy, gastrostomy tube, or aspiration pneumonia at >= 12 months after IMRT. Doses to autosegmented regions of interest (ROIs;inferior, middle and superior constrictors, anterior and posterior digastrics, mylo/geniohyoid complex, intrinsic tongue, and gengioglossus) were obtained from DICOM-RT plans and dose-volume histograms. The probability of chronic RAD as a function of mean ROI dose, stratified by age (<50, 50-59, 60-69, or >70 years), was estimated with logistic probability models and subsequent unsupervised nonlinear curves. Results: Chronic RAD was observed in 34 patients (11%). Age was a significant correlate of chronic RAD, both independently and with dose for all muscle groups examined. Distinct muscle-specific dose-response profiles were observed as a function of age (e.g., 5% of patients in their 50 s [but 20% of those 70 +] who received 60 Gy to the superior constrictor had chronic RAD). This effect was stable across all observed muscle ROIs, with a false discovery rate-corrected p < 0.05, for all dose/muscle/age models, suggesting that including age as a covariate improves modeling of chronic RAD. Conclusions: Age at treatment moderates the probability of chronic RAD after chemo-IMRT for OPSCC, with aging muscles showing lower dose thresholds. Uniform dose constraints may not predict toxicity in older patients. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of European Society for Radiotherapy and Oncology.

关键词： Oropharynx IMRT Radiation Toxicity Presbyphagia

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Imaging of the Axon Initial Segment

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Current Protocols in Neuroscience 2019年第1期89卷 e78页

作者： Di Re, Jessica Kayasandik, Cihan Botello-Lins, Gonzalo Labate, Demetrio Laezza, Fernanda Neuroscience Graduate Program University of Texas Medical Branch Galveston TX United States Department of Pharmacology and Toxicology University of Texas Medical Branch Galveston TX United States Department of Computer Engineering Istanbul Medipol University Istanbul Turkey Biotechnology Program Clear Falls High School Clear Creek Independent School District League City TX United States Department of Mathematics University of Houston Houston TX United States

The axon initial segment (AIS) is the first 20- to 60-μm segment of the axon proximal to the soma of a neuron. This highly specialized subcellular domain is the initiation site of the action potential and contains a high concentration of voltage-gated ion channels held in place by a complex nexus of scaffolding and regulatory proteins that ensure proper electrical activity of the neuron. Studies have shown that dysfunction of many AIS channels and scaffolding proteins occurs in a variety of neuropsychiatric and neurodegenerative diseases, raising the need to develop accurate methods for visualization and quantification of the AIS and its protein content in models of normal and disease conditions. In this article, we describe methods for immunolabeling AIS proteins in cultured neurons and brain slices as well as methods for quantifying protein expression and pattern distribution using fluorescent labeling of these proteins. © 2019 by John Wiley & Sons, Inc. © 2019 John Wiley & Sons, Inc.

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Lattice dynamics localization in low-angle twisted bilayer graphene

arXiv

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arXiv 2020年

作者： Gadelha, Andreij C. Ohlberg, Douglas A.A. Rabelo, Cassiano Neto, Eliel G.S. Vasconcelos, Thiago L. Campos, João L. Lemos, Jessica S. Ornelas, Vinícius Miranda, Daniel Nadas, Rafael Santana, Fabiano C. Watanabe, Kenji Taniguchi, Takashi van Troeye, Benoit Lamparski, Michael Meunier, Vincent Nguyen, Viet-Hung Paszko, Dawid Charlier, Jean-Christophe Campos, Leonardo C. Cançado, Luiz G. Medeiros-Ribeiro, Gilberto Jorio, Ado Physics Department Electrical Engineering Graduate Program Computer Science Department Universidade Federal de Minas Gerais Belo HorizonteMG31270-901 Brazil Physics Institute Universidade Federal da Bahia Campus Universitário de Ondina Salvador - BA40170-115 Brazil Divisão de Metrologia de Materiais Inmetro Duque de Caxias RJ25250-020 Brazil 1-2-1 Sengen Tsukuba-cityIbaraki305-0047 Japan Physics Applied Physics and Astronomy Jonsson Rowland Science Center Room 1C25 110 8th Street TroyNY12180 United States Louvain-la-Neuve Belgium

A low twist angle between the two stacked crystal networks in bilayer graphene enables self-organized lattice reconstruction with the formation of a periodic domain. This superlattice modulates the vibrational and electronic structures, imposing new rules for electron-phonon coupling and the eventual observation of strong correlation and superconductivity. Direct optical images of the crystal superlattice in reconstructed twisted bilayer graphene are reported here, generated by the inelastic scattering of light in a nano-Raman spectroscope. The observation of the crystallographic structure with visible light is made possible due to lattice dynamics localization, the images resembling spectral variations caused by the presence of strain solitons and topological points. The results are rationalized by a nearly-free-phonon model and electronic calculations that highlight the relevance of solitons and topological points, particularly pronounced for structures with small twist angles. We anticipate our discovery to play a role in understanding Jahn-Teller effects and electronic Cooper pairing, among many other important phonon-related effects, and it may be useful for characterizing devices in the most prominent platform for the field of *** Codes 82D03 Copyright © 2020, The Authors. All rights reserved.

关键词： Electronic structure

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A Whole Crow Search Algorithm for Solving Data Clustering

A Whole Crow Search Algorithm for Solving Data Clustering

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International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Ze-Xue Wu Ko-Wei Huang Abba Suganda Girsang Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan R.O.C Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

Data clustering is a well-known data mining approach that usually used to minimizes the intra distance but maximizes inter distance of each data center. The cluster problem has been proved to be an NP-hard problem. In this paper, a hybrid algorithm based on Whole optimization algorithm (WOA) and Crow search algorithm (CSA) is proposed, namely HWCA. The HWCA algorithm has the advantages of the search strategy of the WOA and CSA. In addition to, there are two operators used to improve the quality of solution, namely hybrid individual operator and enhance diversity operator. The hybrid individual operator is used to exchanges individuals from the WOA and CSA systems by using the roulette wheel approach. In other hand, the HWCA performs enhance diversity operator to improve the quality of each system. More over, the HWCA is incorporated with center optimization strategy to enhance diversity of each system. In the performance evaluation, the proposed MPGO algorithm was comparison WOA and CSA algorithm with six well-known UCI benchmarks. The results show that the proposed algorithm has a higher measure of accuracy rate with comparison algorithms.

关键词： Clustering algorithms Optimization Whales Search problems Wheels Benchmark testing Random variables

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Author Correction: Disrupting cellular memory to overcome drug resistance

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Nature communications 2024年第1期15卷 4264页

作者： Guillaume Harmange Raúl A Reyes Hueros Dylan L Schaff Benjamin Emert Michael Saint-Antoine Laura C Kim Zijian Niu Shivani Nellore Mitchell E Fane Gretchen M Alicea Ashani T Weeraratna M Celeste Simon Abhyudai Singh Sydney M Shaffer Cellular and Molecular Biology Graduate Group Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Biochemistry and Molecular Biophysics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Bioengineering School of Engineering and Applied Sciences University of Pennsylvania Philadelphia PA USA. Division of Biology and Biological Engineering California Institute of Technology Pasadena CA 91125 USA. Department of Electrical and Computer Engineering University of Delaware Newark DE 19716 USA. Abramson Family Cancer Research Institute Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Chemistry College of the Arts and Sciences University of Pennsylvania Philadelphia PA USA. Department of Physics College of the Arts and Sciences University of Pennsylvania Philadelphia PA USA. Department of Biology College of the Arts and Sciences University of Pennsylvania Philadelphia PA USA. The Wharton School University of Pennsylvania Philadelphia PA USA. Cancer Signaling and Microenvironment Research Program Fox Chase Cancer Center Philadelphia PA USA. Department of Biochemistry and Molecular Biology Johns Hopkins School of Public Health Baltimore MD USA. Sidney Kimmel Cancer Center Johns Hopkins School of Medicine Baltimore MD USA. Department of Cell and Developmental Biology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Bioengineering School of Engineering and Applied Sciences University of Pennsylvania Philadelphia PA USA. sydshaffer@***. Department of Pathology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. sydshaffer@***.

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

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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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Semi-Supervised Online Elastic Extreme Learning Machine for Data Classification

Semi-Supervised Online Elastic Extreme Learning Machine for ...

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International Joint Conference on Neural Networks

作者： Carlos A. S. da Silva Renato A. Krohling Graduate Program in Computer Science PPGI - Federal University of Espirito Santo (UFES) Vitoria ES Brazil Production Engineering Department Federal University of Espirito Santo (UFES) Vitoria ES Brazil

The Extreme Learning Machine (ELM) is a fast and efficient learning algorithm for single-hidden layer feedforward neural networks (SLFN). Recently, with the increase in data volume in real-world applications, and the need to process data from streaming, two problems have become recurrent in data classification: it is not possible to gather all the necessary data before training the algorithms, and it is difficult to manually label the data for the classification tasks. To address these problems, many variations of ELM have been proposed to allow semi-supervised learning, online sequential learning, or both. In this paper, we propose a variation of ELM called Semi-Supervised Online Elastic Extreme Learning Machine (SSOE-ELM), an algorithm that uses both labeled and unlabeled data to learn in an online sequential way (chunk-by-chunk). We compare our approach to the SOS-ELM in several benchmarks. Our experimental results show that SSOE-ELM outperforms SOS-ELM in accuracy, generalization ability and in training speed.

关键词： Training Partitioning algorithms Matrix decomposition Neurons Heuristic algorithms Laplace equations Cost function

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All-Electrical Determination of Crystal Orientation in Anisotropic Two-Dimensional Materials

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Physical Review Letters 2018年第8期120卷 086801-086801页

作者： Lintao Peng Spencer A. Wells Christopher R. Ryder Mark C. Hersam Matthew Grayson Applied Physics Graduate Program Northwestern University Evanston Illinois 60208 USA Department of Materials Science and Engineering Northwestern University Evanston Illinois 60208 USA Department of Electrical Engineering and Computer Science Northwestern University Evanston Illinois 60208 USA Department of Chemistry Northwestern University Evanston Illinois 60208 USA

The crystal orientation of an exfoliated black phosphorous flake is determined by purely electrical means. A sequence of three resistance measurements on an arbitrarily shaped flake with five contacts determines the three independent components of the anisotropic in-plane resistivity tensor, thereby revealing the crystal axes. The resistivity anisotropy ratio decreases linearly with increasing temperature T and carrier density reaching a maximum ratio of 3.0 at low temperatures and densities, while mobility indicates impurity scattering at low T and acoustic phonon scattering at high T.

关键词： Crystal orientation Electrical conductivity Semiconductors Transport phenomena Multilayer thin films Phosphorene Transition metal dichalcogenides Resistivity measurements

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Exploring Macroscopic Fluctuation of Facial Expression for Mood Disorder Classification

Exploring Macroscopic Fluctuation of Facial Expression for M...

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Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia)

作者： Qian-Bei Hong Chung-Hsien Wu Ming-Hsiang Su Kun-Yi Huang Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Tainan Taiwan Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781538653128

In clinical diagnosis of mood disorder, a large portion of bipolar disorder patients (BDs) are misdiagnosed as unipolar depression (UDs). Clinicians have confirmed that BDs generally show "reduced affect'' during clinical treatment. Thus, it is expected to build an objective and one-time diagnosis system for diagnosis assistance by using machine-learning techniques. In this study, facial expressions of BD, UD and control group (C) elicited by emotional video clips are collected for exploring temporal fluctuation characteristics of intensities of facial muscles expression among the three groups. The differences of facial expressions among mood disorders are investigated by observing macroscopic fluctuations. To deal with these problems, the corresponding methods for feature extraction and modeling are proposed. From the viewpoint of macroscopic facial expression, action unit (AU) is applied for describing the temporal transformation of muscles. Then, modulation spectrum is used for extracting short-term variation of AU. The multilayer perceptron (MLP)-based disorder prediction model is then applied to obtain the prediction results. For evaluation of the proposed method, 12 subjects for three group are included in the K-fold (K=12) cross validation experiments. The experiment results reached 61.1% classification accuracy, and outperformed the other baseline methods.

关键词： Mood Gold Fluctuations Databases Feature extraction Modulation Predictive models

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Syncgan: Synchronize the Latent Spaces of Cross-Modal Generative Adversarial Networks

Syncgan: Synchronize the Latent Spaces of Cross-Modal Genera...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wen-Cheng Chen Chien-Wen Chen Min-Chun Hu Dept. of Computer Science and Information Engineering National Cheng Kung University Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

Generative adversarial network (GAN) has achieved impressive success on cross-domain generation, but it faces difficulty in cross-modal generation due to the lack of a common distribution between heterogeneous data. Most existing methods of conditional based cross-modal GANs adopt the strategy of one-directional transfer and have achieved preliminary success on text-to-image transfer. Instead of learning the transfer between different modalities, we aim to learn a synchronous latent space representing the cross-modal common concept. A novel network component named synchronizer is proposed in this work to judge whether the paired data is synchronous/corresponding or not, which can constrain the latent space of generators in the GANs. Our GAN model, named as SyncGAN, can successfully generate synchronous data (e.g., a pair of image and sound) from identical random noise. For transforming data from one modality to another, we recover the latent code by inverting the mappings of a generator and use it to generate data of different modality. In addition, the proposed model can achieve semi-supervised learning, which makes our model more flexible for practical applications.

关键词： Synchronization Gallium nitride Generators Data models Training Generative adversarial networks Training data

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