226 results on '"Cottrell, Garrison W."'
Search Results
202. The Connectionist Air Guitar: A Dream Come True
- Author
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COTTRELL, GARRISON W., primary
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- 1989
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203. Principal Components Analysis Of Images Via Back Propagation
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Cottrell, Garrison W., primary and Munro, Paul, additional
- Published
- 1988
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204. Parallel Dog Processing: Explorations in the Nanostructure of Dognition
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COTTRELL, GARRISON W., primary
- Published
- 1989
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205. Toward connectionist semantics
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Cottrell, Garrison W., primary
- Published
- 1987
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206. DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data.
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Kim, Hyun Woo, Zhang, Chen, Reher, Raphael, Wang, Mingxun, Alexander, Kelsey L., Nothias, Louis-Félix, Han, Yoo Kyong, Shin, Hyeji, Lee, Ki Yong, Lee, Kyu Hyeong, Kim, Myeong Ji, Dorrestein, Pieter C., Gerwick, William H., and Cottrell, Garrison W.
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NUCLEAR magnetic resonance , *MOLECULAR structure , *NUCLEAR structure , *CONVOLUTIONAL neural networks , *NUCLEAR magnetic resonance spectroscopy , *SINGLE molecule magnets , *CHEMICAL shift (Nuclear magnetic resonance) - Abstract
The identification of molecular structure is essential for understanding chemical diversity and for developing drug leads from small molecules. Nevertheless, the structure elucidation of small molecules by Nuclear Magnetic Resonance (NMR) experiments is often a long and non-trivial process that relies on years of training. To achieve this process efficiently, several spectral databases have been established to retrieve reference NMR spectra. However, the number of reference NMR spectra available is limited and has mostly facilitated annotation of commercially available derivatives. Here, we introduce DeepSAT, a neural network-based structure annotation and scaffold prediction system that directly extracts the chemical features associated with molecular structures from their NMR spectra. Using only the 1H-13C HSQC spectrum, DeepSAT identifies related known compounds and thus efficiently assists in the identification of molecular structures. DeepSAT is expected to accelerate chemical and biomedical research by accelerating the identification of molecular structures. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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207. DeePr-ESN: A deep projection-encoding echo-state network.
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Ma, Qianli, Shen, Lifeng, and Cottrell, Garrison W.
- Subjects
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RECURRENT neural networks , *TIME series analysis , *DIGITAL video , *DEEP learning - Abstract
• We develop a novel hierarchical reservoir computing (RC) framework called the Deep Projection-encoding Echo State Network (DeePr-ESN) based on projection-encodings between reservoirs, which takes advantage of the merits of reservoir computing and deep learning, and bridges the gap between them. • By unsupervised encoding of echo states layer by layer, the proposed DeePr-ESN can not only provide more robust generalization performance than existing methods, but also obtains more rich multiscale dynamics than other hierarchical RC models. • Compared with the existing Reservoir Computing hierarchical models, the DeePr-ESN achieves better performance on well-known chaotic time series modeling tasks and several real-world time series prediction tasks. Highlights (for review) As a recurrent neural network that requires no training, the reservoir computing (RC) model has attracted widespread attention in the last decade, especially in the context of time series prediction. However, most time series have a multiscale structure, which a single-hidden-layer RC model may have difficulty capturing. In this paper, we propose a novel multiple projection-encoding hierarchical reservoir computing framework called Deep Projection-encoding Echo State Network (DeePr-ESN). The most distinctive feature of our model is its ability to learn multiscale dynamics through stacked ESNs, connected via subspace projections. Specifically, when an input time series is projected into the high-dimensional echo-state space of a reservoir, a subsequent encoding layer (e.g., an autoencoder or PCA) projects the echo-state representations into a lower-dimensional feature space. These representations are the principal components of the echo-state representations, which removes the high frequency components of the representations. These can then be processed by another ESN through random connections. By using projection layers and encoding layers alternately, our DeePr-ESN can provide much more robust generalization performance than previous methods, and also fully takes advantage of the temporal kernel property of ESNs to encode the multiscale dynamics of time series. In our experiments, the DeePr-ESNs outperform both standard ESNs and existing hierarchical reservoir computing models on some artificial and real-world time series prediction tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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208. COMPUTER SCIENCE: New Life for Neural Networks.
- Author
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Cottrell, Garrison W.
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ARTIFICIAL neural networks , *SELF-organizing maps , *LATENT structure analysis , *LATENT variables , *EVOLUTIONARY computation , *EUCLIDEAN algorithm , *NUMBER theory , *ARTIFICIAL intelligence , *COMPUTER science research - Abstract
The article offers information concerning the utilization of neural networks in dealing with high-dimensional data. A case is presented showing a large number of three-dimensional points in random order. An important feature of the case has been emphasized that the natural distance between the two points is not the Euclidian straight line distance but rather the distance along the curve. It has been stated that standard neural techniques must be conducted to discover the low dimensional encoding of very high-dimensional data. The neural network technique that has been stated is developed by Hinton's group in which use networks initialized to be near a solution, using unsupervised methods.
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- 2006
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209. Degenerative grammar: The story of Outa.
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Cottrell, Garrison W.
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GENERATIVE grammar ,ANIMAL communication - Abstract
Proposes degenerative grammar (DG) as an alternative approach to generative grammar, whose approaches to the study of dog language have failed in their accounts of polysemy and the development of language comprehension. Meaning is deceptualization as the basic assumption of DG; Hypothesis that the central mold is built out of the young dog's initial experiences.
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- 1996
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210. Topics in DSP (dog signal processing).
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Abbott, Grayson D. and Cottrell, Garrison W.
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SIGNAL processing - Abstract
Presents a humorous article about dog signal processing (DSP). Need for a good bark recognition; Advantage of working on bark recognition; Basis of the bark recognition system.
- Published
- 1991
211. Multimodal Wildland Fire Smoke Detection.
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Bhamra, Jaspreet Kaur, Anantha Ramaprasad, Shreyas, Baldota, Siddhant, Luna, Shane, Zen, Eugene, Ramachandra, Ravi, Kim, Harrison, Schmidt, Chris, Arends, Chris, Block, Jessica, Perez, Ismael, Crawl, Daniel, Altintas, Ilkay, Cottrell, Garrison W., and Nguyen, Mai H.
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WILDFIRES , *FIRE detectors , *WILDFIRE prevention , *SMOKE , *DEEP learning , *OPTICAL images , *WILDFIRE risk - Abstract
Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have, in turn, led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgent need to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. To that end, in this paper, we present our work on integrating multiple data sources into SmokeyNet, a deep learning model using spatiotemporal information to detect smoke from wildland fires. We present Multimodal SmokeyNet and SmokeyNet Ensemble for multimodal wildland fire smoke detection using satellite-based fire detections, weather sensor measurements, and optical camera images. An analysis is provided to compare these multimodal approaches to the baseline SmokeyNet in terms of accuracy metrics, as well as time-to-detect, which is important for the early detection of wildfires. Our results show that incorporating weather data in SmokeyNet improves performance numerically in terms of both F1 and time-to-detect over the baseline with a single data source. With a time-to-detect of only a few minutes, SmokeyNet can be used for automated early notification of wildfires, providing a useful tool in the fight against destructive wildfires. [ABSTRACT FROM AUTHOR]
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- 2023
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212. Learning consensus representations in multi-latent spaces for multi-view clustering.
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Ma, Qianli, Zheng, Jiawei, Li, Sen, Zheng, Zhenjing, and Cottrell, Garrison W.
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LATENT class analysis (Statistics) , *OPEN-ended questions - Abstract
Multi-view clustering integrates features from different views to perform clustering. This problem has attracted increasing attention in recent years because multi-view data has become more common. The mainstream methods focus on learning a common representation or decoupling the view-specific and the shared representations in subspaces and then performing clustering on the fused results. However, it is still an open question how to best enforce that the learned representations possess good clustering properties and thus improve the clustering performance. In this paper, we propose a novel unsupervised model called Deep Multi-view Consensus Clustering (DMCC) to learn consensus view-specific representations in multiple latent spaces, where specificity and consistency are jointly retained for representation learning. For each view, DMCC learns view-specific representations in individual latent spaces with the help of a reconstruction target and a soft K-means objective. Furthermore, by aligning the cluster indicator matrices of each view, DMCC encourages consensus across views and enables one view to get help from other views to guide its representation learning. Thus, the learned representations are cluster-friendly within each view, and consistent across views. The proposed method achieves state-of-the-art performance in four metrics on extensive datasets. Among all datasets, our proposed method DMCC achieves an average of 2.6% and 2.5% better performance than state-of-the-art methods in RI and NMI, respectively. We also visualize the learned representations to show that our approach does learn cluster-friendly representations and to demonstrate the effectiveness of encouraging mutual consensus across views. [ABSTRACT FROM AUTHOR]
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- 2024
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213. The ventral striatum dissociates information expectation, reward anticipation, and reward receipt.
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Filimon, Flavia, Nelson, Jonathan D., Sejnowski, Terrence J., Sereno, Martin I., and Cottrell, Garrison W.
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EXPECTED utility , *DECISION theory , *NUCLEUS accumbens , *FUNCTIONAL magnetic resonance imaging , *PREFRONTAL cortex - Abstract
Do dopaminergic reward structures represent the expected utility of information similarly to a reward? Optimal experimental design models from Bayesian decision theory and statistics have proposed a theoretical framework for quantifying the expected value of information that might result from a query. In particular, this formulation quantifies the value of information before the answer to that query is known, in situations where payoffs are unknown and the goal is purely epistemic: That is, to increase knowledge about the state of the world. Whether and how such a theoretical quantity is represented in the brain is unknown. Here we use an event-related functional MRI (fMRI) task design to disentangle information expectation, information revelation and categorization outcome anticipation, and response-contingent reward processing in a visual probabilistic categorization task. We identify a neural signature corresponding to the expectation of information, involving the left lateral ventral striatum. Moreover, we show a temporal dissociation in the activation of different reward-related regions, including the nucleus accumbens, medial prefrontal cortex, and orbitofrontal cortex, during information expectation versus reward-related processing. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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214. Author Correction: Small Molecule Accurate Recognition Technology (SMART) to Enhance Natural Products Research.
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Zhang, Chen, Idelbayev, Yerlan, Roberts, Nicholas, Tao, Yiwen, Nannapaneni, Yashwanth, Duggan, Brendan M., Min, Jie, Lin, Eugene C., Gerwick, Erik C., Cottrell, Garrison W., and Gerwick, William H.
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SMALL molecules , *NATURAL products - Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]
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- 2020
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215. Hierarchical Cellular Automata for Visual Saliency.
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Qin, Yao, Feng, Mengyang, Lu, Huchuan, and Cottrell, Garrison W.
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CELLULAR automata , *COMPUTER vision , *SEMANTICS , *DEEP learning , *BAYESIAN analysis - Abstract
Saliency detection, finding the most important parts of an image, has become increasingly popular in computer vision. In this paper, we introduce Hierarchical Cellular Automata (HCA)—a temporally evolving model to intelligently detect salient objects. HCA consists of two main components: Single-layer Cellular Automata (SCA) and Cuboid Cellular Automata (CCA). As an unsupervised propagation mechanism, Single-layer Cellular Automata can exploit the intrinsic relevance of similar regions through interactions with neighbors. Low-level image features as well as high-level semantic information extracted from deep neural networks are incorporated into the SCA to measure the correlation between different image patches. With these hierarchical deep features, an impact factor matrix and a coherence matrix are constructed to balance the influences on each cell’s next state. The saliency values of all cells are iteratively updated according to a well-defined update rule. Furthermore, we propose CCA to integrate multiple saliency maps generated by SCA at different scales in a Bayesian framework. Therefore, single-layer propagation and multi-scale integration are jointly modeled in our unified HCA. Surprisingly, we find that the SCA can improve all existing methods that we applied it to, resulting in a similar precision level regardless of the original results. The CCA can act as an efficient pixel-wise aggregation algorithm that can integrate state-of-the-art methods, resulting in even better results. Extensive experiments on four challenging datasets demonstrate that the proposed algorithm outperforms state-of-the-art conventional methods and is competitive with deep learning based approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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216. Small Molecule Accurate Recognition Technology (SMART) to Enhance Natural Products Research.
- Author
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Zhang, Chen, Idelbayev, Yerlan, Roberts, Nicholas, Tao, Yiwen, Nannapaneni, Yashwanth, Duggan, Brendan M., Min, Jie, Lin, Eugene C., Gerwick, Erik C., Cottrell, Garrison W., and Gerwick, William H.
- Abstract
Various algorithms comparing 2D NMR spectra have been explored for their ability to dereplicate natural products as well as determine molecular structures. However, spectroscopic artefacts, solvent effects, and the interactive effect of functional group(s) on chemical shifts combine to hinder their effectiveness. Here, we leveraged Non-Uniform Sampling (NUS) 2D NMR techniques and deep Convolutional Neural Networks (CNNs) to create a tool, SMART, that can assist in natural products discovery efforts. First, an NUS heteronuclear single quantum coherence (HSQC) NMR pulse sequence was adapted to a state-of-the-art nuclear magnetic resonance (NMR) instrument, and data reconstruction methods were optimized, and second, a deep CNN with contrastive loss was trained on a database containing over 2,054 HSQC spectra as the training set. To demonstrate the utility of SMART, several newly isolated compounds were automatically located with their known analogues in the embedded clustering space, thereby streamlining the discovery pipeline for new natural products. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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217. FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection.
- Author
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Dewangan, Anshuman, Pande, Yash, Braun, Hans-Werner, Vernon, Frank, Perez, Ismael, Altintas, Ilkay, Cottrell, Garrison W., and Nguyen, Mai H.
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WILDFIRES , *DEEP learning , *FIRE detectors , *SMOKE , *WILDFIRE prevention , *COMPUTER vision , *STIMULUS & response (Psychology) - Abstract
The size and frequency of wildland fires in the western United States have dramatically increased in recent years. On high-fire-risk days, a small fire ignition can rapidly grow and become out of control. Early detection of fire ignitions from initial smoke can assist the response to such fires before they become difficult to manage. Past deep learning approaches for wildfire smoke detection have suffered from small or unreliable datasets that make it difficult to extrapolate performance to real-world scenarios. In this work, we present the Fire Ignition Library (FIgLib), a publicly available dataset of nearly 25,000 labeled wildfire smoke images as seen from fixed-view cameras deployed in Southern California. We also introduce SmokeyNet, a novel deep learning architecture using spatiotemporal information from camera imagery for real-time wildfire smoke detection. When trained on the FIgLib dataset, SmokeyNet outperforms comparable baselines and rivals human performance. We hope that the availability of the FIgLib dataset and the SmokeyNet architecture will inspire further research into deep learning methods for wildfire smoke detection, leading to automated notification systems that reduce the time to wildfire response. [ABSTRACT FROM AUTHOR]
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- 2022
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218. Approximate Spatial Layout Processing in Early Vision
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Hucka, Michael, Kaplan, Stephen, and Cottrell, Garrison W.
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GeneralLiterature_MISCELLANEOUS - Abstract
Imagine yourself running through rough terrain, perhaps fleeing a predator, or perhaps chasing after prey. Your visual system does not have time to scrutinize the countless trees, rocks, and other objects you pass by. What you need most is enough spatial information to avoid obstacles, to orient yourself, to pick a path. In this situation, even a rough sketch of the spatial layout of the environment can provide crucial information.
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- 1996
219. PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning.
- Author
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Gahl M, Kim HW, Glukhov E, Gerwick WH, and Cottrell GW
- Subjects
- Humans, Cytostatic Agents pharmacology, Carya, Biological Products pharmacology, Deep Learning, Neoplasms
- Abstract
Many machine learning techniques are used as drug discovery tools with the intent to speed characterization by determining relationships between compound structure and biological function. However, particularly in anticancer drug discovery, these models often make only binary decisions about the biological activity for a narrow scope of drug targets. We present a feed-forward neural network, PECAN (Prediction Engine for the Cytostatic Activity of Natural product-like compounds), that simultaneously classifies the potential antiproliferative activity of compounds against 59 cancer cell lines. It predicts the activity to be one of six categories, indicating not only if activity is present but the degree of activity. Using an independent subset of NCI data as a test set, we show that PECAN can reach 60.1% accuracy in a six-way classification and present further evidence that it classifies based on useful structural features of compounds using a "within-one" measure that reaches 93.0% accuracy.
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- 2024
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220. SMART-Miner: A convolutional neural network-based metabolite identification from 1 H- 13 C HSQC spectra.
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Kim HW, Zhang C, Cottrell GW, and Gerwick WH
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- Complex Mixtures, Magnetic Resonance Spectroscopy methods, Neural Networks, Computer, Metabolome, Metabolomics methods
- Abstract
The identification of metabolites from complex biofluids and extracts of tissues is an essential process for understanding metabolic profiles. Nuclear magnetic resonance (NMR) spectroscopy is widely used in metabolomics studies for identification and quantification of metabolites. However, the accurate identification of individual metabolites is still a challenging process with higher peak intensity or similar chemical shifts from different metabolites. In this study, we applied a convolutional neural network (CNN) to
1 H-13 C HSQC NMR spectra to achieve accurate peak identification in complex mixtures. The results reveal that the neural network was successfully trained on metabolite identification from these 2D NMR spectra and achieved very good performance compared with other NMR-based metabolomic tools., (© 2021 John Wiley & Sons, Ltd.)- Published
- 2022
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221. NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products.
- Author
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Kim HW, Wang M, Leber CA, Nothias LF, Reher R, Kang KB, van der Hooft JJJ, Dorrestein PC, Gerwick WH, and Cottrell GW
- Subjects
- Biosynthetic Pathways, Biological Products chemistry, Biological Products classification, Neural Networks, Computer
- Abstract
Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing for NP structures. An ideal semantic ontology for the classification of NPs should go beyond the simple presence/absence of chemical substructures, but also include the taxonomy of the producing organism, the nature of the biosynthetic pathway, and/or their biological properties. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs, and especially so when analyzing large numbers of NPs. Here, we introduce NPClassifier, a deep-learning tool for the automated structural classification of NPs from their counted Morgan fingerprints. NPClassifier is expected to accelerate and enhance NP discovery by linking NP structures to their underlying properties.
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- 2021
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222. Pagoamide A, a Cyclic Depsipeptide Isolated from a Cultured Marine Chlorophyte, Derbesia sp., Using MS/MS-Based Molecular Networking.
- Author
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Li Y, Yu HB, Zhang Y, Leao T, Glukhov E, Pierce ML, Zhang C, Kim H, Mao HH, Fang F, Cottrell GW, Murray TF, Gerwick L, Guan H, and Gerwick WH
- Subjects
- American Samoa, Amino Acids, Animals, Biological Products isolation & purification, Depsipeptides isolation & purification, Female, Molecular Structure, Rats, Tandem Mass Spectrometry, Biological Products chemistry, Chlorophyta chemistry, Depsipeptides chemistry
- Abstract
A thiazole-containing cyclic depsipeptide with 11 amino acid residues, named pagoamide A ( 1 ), was isolated from laboratory cultures of a marine Chlorophyte, Derbesia sp. This green algal sample was collected from America Samoa, and pagoamide A was isolated using guidance by MS/MS-based molecular networking. Cultures were grown in a light- and temperature-controlled environment and harvested after several months of growth. The planar structure of pagoamide A ( 1 ) was characterized by detailed 1D and 2D NMR experiments along with MS and UV analysis. The absolute configurations of its amino acid residues were determined by advanced Marfey's analysis following chemical hydrolysis and hydrazinolysis reactions. Two of the residues in pagoamide A ( 1 ), phenylalanine and serine, each occurred twice in the molecule, once in the d- and once in the l-configuration. The biosynthetic origin of pagoamide A ( 1 ) was considered in light of other natural products investigations with coenocytic green algae.
- Published
- 2020
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223. A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products.
- Author
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Reher R, Kim HW, Zhang C, Mao HH, Wang M, Nothias LF, Caraballo-Rodriguez AM, Glukhov E, Teke B, Leao T, Alexander KL, Duggan BM, Van Everbroeck EL, Dorrestein PC, Cottrell GW, and Gerwick WH
- Subjects
- Biological Products isolation & purification, Biological Products toxicity, Cell Line, Tumor, Cheminformatics, Cyanobacteria chemistry, Humans, Magnetic Resonance Spectroscopy, Peptides, Cyclic chemistry, Peptides, Cyclic isolation & purification, Peptides, Cyclic toxicity, Biological Products chemistry, Machine Learning, Neural Networks, Computer
- Abstract
This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS
2 -based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A-I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing "atomic sort" method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.- Published
- 2020
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224. Central and peripheral vision for scene recognition: A neurocomputational modeling exploration.
- Author
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Wang P and Cottrell GW
- Subjects
- Humans, Learning, Photic Stimulation methods, Neural Networks, Computer, Pattern Recognition, Visual physiology, Visual Perception physiology
- Abstract
What are the roles of central and peripheral vision in human scene recognition? Larson and Loschky (2009) showed that peripheral vision contributes more than central vision in obtaining maximum scene recognition accuracy. However, central vision is more efficient for scene recognition than peripheral, based on the amount of visual area needed for accurate recognition. In this study, we model and explain the results of Larson and Loschky (2009) using a neurocomputational modeling approach. We show that the advantage of peripheral vision in scene recognition, as well as the efficiency advantage for central vision, can be replicated using state-of-the-art deep neural network models. In addition, we propose and provide support for the hypothesis that the peripheral advantage comes from the inherent usefulness of peripheral features. This result is consistent with data presented by Thibaut, Tran, Szaffarczyk, and Boucart (2014), who showed that patients with central vision loss can still categorize natural scenes efficiently. Furthermore, by using a deep mixture-of-experts model ("The Deep Model," or TDM) that receives central and peripheral visual information on separate channels simultaneously, we show that the peripheral advantage emerges naturally in the learning process: When trained to categorize scenes, the model weights the peripheral pathway more than the central pathway. As we have seen in our previous modeling work, learning creates a transform that spreads different scene categories into different regions in representational space. Finally, we visualize the features for the two pathways, and find that different preferences for scene categories emerge for the two pathways during the training process.
- Published
- 2017
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225. Observed, Executed, and Imagined Action Representations can be Decoded From Ventral and Dorsal Areas.
- Author
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Filimon F, Rieth CA, Sereno MI, and Cottrell GW
- Subjects
- Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Nerve Net blood supply, Nerve Net physiology, Observation, Oxygen blood, Parietal Lobe blood supply, Prefrontal Cortex blood supply, Psychomotor Performance, Brain Mapping, Executive Function physiology, Imagination physiology, Movement physiology, Parietal Lobe physiology, Prefrontal Cortex physiology
- Abstract
Previous functional magnetic resonance imaging (fMRI) research on action observation has emphasized the role of putative mirror neuron areas such as Broca's area, ventral premotor cortex, and the inferior parietal lobule. However, recent evidence suggests action observation involves many distributed cortical regions, including dorsal premotor and superior parietal cortex. How these different regions relate to traditional mirror neuron areas, and whether traditional mirror neuron areas play a special role in action representation, is unclear. Here we use multi-voxel pattern analysis (MVPA) to show that action representations, including observation, imagery, and execution of reaching movements: (1) are distributed across both dorsal (superior) and ventral (inferior) premotor and parietal areas; (2) can be decoded from areas that are jointly activated by observation, execution, and imagery of reaching movements, even in cases of equal-amplitude blood oxygen level-dependent (BOLD) responses; and (3) can be equally accurately classified from either posterior parietal or frontal (premotor and inferior frontal) regions. These results challenge the presumed dominance of traditional mirror neuron areas such as Broca's area in action observation and action representation more generally. Unlike traditional univariate fMRI analyses, MVPA was able to discriminate between imagined and observed movements from previously indistinguishable BOLD activations in commonly activated regions, suggesting finer-grained distributed patterns of activation., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)
- Published
- 2015
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226. NIMBLE: a kernel density model of saccade-based visual memory.
- Author
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Barrington L, Marks TK, Hsiao JH, and Cottrell GW
- Subjects
- Bayes Theorem, Face, Female, Fixation, Ocular, Humans, Male, Pattern Recognition, Visual, Probability, Recognition, Psychology, Reproducibility of Results, Memory physiology, Models, Psychological, Saccades physiology, Visual Perception physiology
- Abstract
We present a Bayesian version of J. Lacroix, J. Murre, and E. Postma's (2006) Natural Input Memory (NIM) model of saccadic visual memory. Our model, which we call NIMBLE (NIM with Bayesian Likelihood Estimation), uses a cognitively plausible image sampling technique that provides a foveated representation of image patches. We conceive of these memorized image fragments as samples from image class distributions and model the memory of these fragments using kernel density estimation. Using these models, we derive class-conditional probabilities of new image fragments and combine individual fragment probabilities to classify images. Our Bayesian formulation of the model extends easily to handle multi-class problems. We validate our model by demonstrating human levels of performance on a face recognition memory task and high accuracy on multi-category face and object identification. We also use NIMBLE to examine the change in beliefs as more fixations are taken from an image. Using fixation data collected from human subjects, we directly compare the performance of NIMBLE's memory component to human performance, demonstrating that using human fixation locations allows NIMBLE to recognize familiar faces with only a single fixation.
- Published
- 2008
- Full Text
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