Label-free discrimination of tumorigenesis stages using in vitro prostate cancer bone metastasis model by Raman imaging by Kar, Sumanta, Jaswandkar, Sharad V., Katti, Kalpana S., Kang, Jeon Woong, So, Peter T. C., Paulmurugan, Ramasamy, Liepmann, Dorian, Venkatesan, Renugopalakrishnan, Katti, Dinesh R.

“…Our results showed distinctive spectral differences in Raman imaging between prostate cancer cells and the cells cultured in 3D bone mimetic scaffolds, particularly at 1002, 1261, 1444, and 1654 cm<jats:sup>−1</jats:sup>, which primarily contain proteins and lipids signals. Raman maps capture sub-cellular responses with the progression of tumor cells into metastasis. …”
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Uncovering Fast Solid-Acid Proton Conductors Based on Dynamics of Polyanion Groups and Proton Bonding Strength by Žguns, Pjotrs, Klyukin, Konstantin, Wang, Louis S., Xiong, Grace, Li, Ju, Haile, Sossina M., Yildiz, Bilge

“…In this work, we have mapped the structural, chemical and dynamic properties of solid acids to the elementary steps of the Grotthuss mechanism of proton diffusion. …”
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Engineering synthetic and recombinant human lysosomal β-glucocerebrosidase for enzyme replacement therapy for Gaucher disease by Figueiredo, Lílian L. S., Junior, Wilson L., da Silva Goncalves, Victor W., Ramos, Ester S., D’Almeida, Vania, de Souza, Lucas E. B., Orellana, Maristela D., Abraham, Kuruvilla J., Lichtenstein, Flávio, Bleicher, Lucas

“…We also evaluated conserved and coevolved residues mapped to functionally important positions. Further studies are needed to assess its functionality in a GD animal model. …”
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Information FOMO: The Unhealthy Fear of Missing Out on Information&mdash;A Method for Removing Misleading Data for Healthier Models by Pickering, Ethan, Sapsis, Themistoklis P.

“…We find these instabilities are a result of the complexity of the underlying map and are linked to extreme events and heavy tails. …”
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Non-invasive estimation of the powder size distribution from a single speckle image by Zhang, Qihang, Pandit, Ajinkya, Liu, Zhiguang, Guo, Zhen, Muddu, Shashank, Wei, Yi, Pereg, Deborah, Nazemifard, Neda, Papageorgiou, Charles, Yang, Yihui, Tang, Wenlong, Braatz, Richard D, Myerson, Allan S, Barbastathis, George

“…The latter approach requires an inverse map from the speckle autocorrelation to the particle sizes. …”
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Neural Light Transport for Relighting and View Synthesis by Zhang, Xiuming, Fanello, Sean, Tsai, Yun-Ta, Sun, Tiancheng, Xue, Tianfan, Pandey?, Rohit, Orts-Escolano, Sergio, Davidson?, Philip, Rhemann, Christoph, Debevec?, Paul, Barron, Jonathan T., Ramamoorthi, Ravi, Freeman, William

“…With this learned LT, one can relight the scene photorealistically with a directional light or an HDRI map, synthesize novel views with view-dependent effects, or do both simultaneously, all in a unified framework using a set of sparse observations.…”
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RoboGrammar: Graph Grammar for Terrain-Optimized Robot Design by Zhao, Allan, Xu, Jie, Konakovic-Lukovic, Mina, Hughes, Josephine, Spielberg, Andrew, Rus, Daniela, Matusik, Wojciech

“…In Graph Heuristic Search, we explore the design space while simultaneously learning a function that maps incomplete designs (e.g., nodes in the combinatorial search tree) to the best performance values that can be achieved by expanding these incomplete designs. …”
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Television food advertising towards children : a content analysis of the Singapore advertising landscape and experimental study on the placement of pro-nutritional advertisements by Lim, Glenn Yin De., Lin, Jieying., Tang, Shuxia., Chen, Wendy Jin Jing.

“…This study aims to examine food and beverage (F&B) advertising as well as persuasion strategies in health communication in the form of pro-nutritional advertisements, in the context of children’s television viewing. In Study One, we mapped out the food advertising landscape by content analyzing F&B advertisements (ads) across six television channels over two weeks in Singapore. …”
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Risk assessment model for UAV cost-effective path planning in urban environments by Hu, Xinting, Pang, Bizhao, Dai, Fuqing, Low, Kin Huat

“…We then computed the total risk and generated a risk cost map for path planning. Modified path planning algorithms were used to produce a cost-effective path, and we compared their performances in terms of total risk cost and computational time. …”
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Rare bioparticle detection via deep metric learning by Luo, Shaobo, Shi, Yuzhi, Chin, Lip Ket, Zhang, Yi, Wen, Bihan, Sun, Ying, Nguyen, Binh T. T., Chierchia, Giovanni, Talbot, Hugues, Bourouina, Tarik, Jiang, Xudong, Liu, Ai-Qun

“…Deep metric learning offers a better generatability by using distance information to model the similarity of the images and learning function maps from image pixels to a latent space, playing a vital role in rare object detection. …”
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Vehicle re-identification using machine learning by Tang, Lisha

“…Experimental results show that our approach outperforms recent state-of-the-art methods by averagely 2.63% in CMC@1 on VehicleID and 2.2% in mAP on VeRi776.…”
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Deep Koopman operator-informed safety command governor for autonomous vehicles by Chen, Hao, He, Xiangkun, Cheng, Shuo, Lv, Chen

“…However, Koopman operator maps the original nonlinear system into an infinite-dimensional linear space to achieve global linearization of the nonlinear system through input and output data, which derives an absolute equivalent linear representation of the original state space. …”
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Development of the polyglot Asian medicine knowledge graph system by Khoo, Christopher S. G., Stanley-Baker, Michael, Faizah Binte Zakaria, Chen, Jinju, Ang, Shaun Qiao Rou, Huang, Bo

“…The paper describes how the knowledge graph was derived based on a dataset comprising over 25 Microsoft Excel spreadsheets, and how the spreadsheet data were processed and mapped to the graph database using upload scripts in the Neo4j Cypher graph query language. …”
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Super enhancer acquisition drives expression of oncogenic PPP1R15B that regulates protein homeostasis in multiple myeloma by Xiong, Sinan, Zhou, Jianbiao, Tan, Tze King, Chung, Tae-Hoon, Tan, Tuan Zea, Toh, Sabrina Hui-Min, Tang, Nicole Xin Ning, Jia, Yunlu, See, Yi Xiang, Fullwood, Melissa Jane, Sanda, Takaomi, Chng, Wee-Joo

“…Here we generate a high-resolution contact map of myeloma-associated super-enhancers by integrating H3K27ac ChIP-seq and HiChIP from myeloma cell lines, patient-derived myeloma cells and normal plasma cells. …”
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Smart sleep monitoring: sparse sensor-based spatiotemporal CNN for sleep posture detection by Hu, Dikun, Gao, Weidong, Ang, Kai Keng, Hu, Mengjiao, Chuai, Gang, Huang, Rong

“…This S3CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. …”
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Secure image encryption based on an ideal new nonlinear discrete dynamical system by Lin, M. Rongming, Ng, Teng Yong

“…It is well known that encryption algorithms developed based on Logistic map suffer from limited key space due to the narrow regions of system parameters which can be used, potential risk of security in the presence of numerous periodic windows within the key space, and weakness in known-plain-text attack due to the inherent correlation among the chaotic sequence used for encryption. …”
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Canonical Diffeomorphisms of Manifolds Near Spheres by Wang, Bing, Zhao, Xinrui

“…Let $$\{f_i\}_{i=1}^{n+1}$$ { f i } i = 1 n + 1 be the first $$(n+1)$$ ( n + 1 ) -eigenfunctions of (M, g) and $$f=(f_1, f_2, \ldots , f_{n+1})$$ f = ( f 1 , f 2 , … , f n + 1 ) . Then the map $${\tilde{f}}=\frac{f}{|f|}: M \rightarrow S^n$$ f ~ = f | f | : M → S n provides a diffeomorphism, and $${\tilde{f}}$$ f ~ satisfies a uniform bi-Hölder estimate. …”
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Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms by Ragnarsdottir, Hanna, Ozkan, Ece, Michel, Holger, Chin-Cheong, Kieran, Manduchi, Laura, Wellmann, Sven, Vogt, Julia E.

“…We complement our predictions with saliency maps and show that the learned model focuses on clinically relevant cardiac structures, motivating its usage in clinical practice. …”
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Petz recovery from subsystems in conformal field theory by Vardhan, Shreya, Wei, Annie Y., Zou, Yijian

“…We use an explicit recovery channel known as the twirled Petz map, and study distance measures such as the fidelity, relative entropy, and trace distance between the original state and the recovered state. …”
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Identification of a Gene Signature That Predicts Dependence upon YAP/TAZ-TEAD by Kanai, Ryan, Norton, Emily, Stern, Patrick, Hynes, Richard O., Lamar, John M.

“…We found that the genes in this signature are TEAD-dependent in several melanoma cell lines, and that their expression strongly correlates with YAP/TAZ activation in human melanomas. Using DepMap dependency data, we found that this YAP/TAZ signature was predictive of melanoma cell dependence upon YAP/TAZ or TEADs. …”
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