jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jasenko Zivanov | Takanori Nakane | Sjors H. W. Scheres
Source: IUCrJ (article, 2018-11-08)
Field: Materials Science — Surfaces, Coatings and Films

A Bayesian approach to beam-induced motion correction in cryo-EM single-particle analysis A new method to estimate the trajectories of particle motion and the amount of cumulative beam damage in electron cryo-microscopy (cryo-EM) single-particle analysis is presented. The motion within the sample is modelled through the use of Gaussian process regression. This allows a prior likelihood that favours spatially and temporally smooth motion to be associated with each hypothetical set of particle trajectories without imposing hard constraints. This formulation enables the <i>a posteriori</i> likelihood of a set of particle trajectories to be expressed as a product of that prior likelihood and an observation likelihood given by the data, and this <i>a posteriori</i> likelihood to then be maximized. Since the smoothness prior requires three parameters that describe the statistics of the observed motion, an efficient stochastic method to estimate these parameters is also proposed. Finally, a practical algorithm is proposed that estimates the average amount of cumulative radiation damage as a function of radiation dose and spatial frequency, and then fits relative <i>B</i> factors to that damage in a robust way. The method is evaluated on three publicly available data sets, and its usefulness is illustrated by comparison with state-of-the-art methods and previously published results. The new method has been implemented as Bayesian polishing in <i>RELION</i>-3, where it replaces the existing particle-polishing method, as it outperforms the latter in all tests conducted. Smoothness ; Maximum a posteriori estimation ; Bayesian probability ; Likelihood function ; Computer science ; Expectation–maximization algorithm ; Kriging ; Algorithm ; A priori and a posteriori ; Particle (ecology) ; Statistics ; Mathematics ; Artificial intelligence ; Maximum likelihood ; Estimation theory ; Machine learning Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Christina Ludwig | Ludovic Gillet | George Rosenberger | Sabine Amon | Ben C. Collins | Ruedi Aebersold
Source: Molecular Systems Biology (review, 2018-08-01)
Field: Chemistry — Spectroscopy

Data‐independent acquisition‐based <scp>SWATH</scp> ‐ <scp>MS</scp> for quantitative proteomics: a tutorial Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH-MS is a specific variant of data-independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH-MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH-MS data, a strategy based on peptide-centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH-MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH-MS data using peptide-centric scoring. Furthermore, concepts on how to improve SWATH-MS data acquisition, potential trade-offs of parameter settings and alternative data analysis strategies are discussed. Library science ; Systems biology ; Biology ; Computational biology ; Computer science Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Florian Meier | Andreas-David Brunner | Scarlet Koch | Heiner Koch | Markus Lubeck | Michael Krause | Niels Goedecke | Jens Decker | Thomas Kosinski | Melvin A. Park | Nicolai Bache | Ole Hoerning | Jürgen Cox | Oliver Räther | Matthias Mann
Source: Molecular & Cellular Proteomics (article, 2018-11-05)
Field: Chemistry — Spectroscopy

Online Parallel Accumulation–Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer In bottom-up proteomics, peptides are separated by liquid chromatography with elution peak widths in the range of seconds, whereas mass spectra are acquired in about 100 microseconds with time-of-flight (TOF) instruments. This allows adding ion mobility as a third dimension of separation. Among several formats, trapped ion mobility spectrometry (TIMS) is attractive because of its small size, low voltage requirements and high efficiency of ion utilization. We have recently demonstrated a scan mode termed parallel accumulation - serial fragmentation (PASEF), which multiplies the sequencing speed without any loss in sensitivity (Meier <i>et al.</i>, PMID: 26538118). Here we introduce the timsTOF Pro instrument, which optimally implements online PASEF. It features an orthogonal ion path into the ion mobility device, limiting the amount of debris entering the instrument and making it very robust in daily operation. We investigate different precursor selection schemes for shotgun proteomics to optimally allocate in excess of 100 fragmentation events per second. More than 600,000 fragmentation spectra in standard 120 min LC runs are achievable, which can be used for near exhaustive precursor selection in complex mixtures or accumulating the signal of weak precursors. In 120 min single runs of HeLa digest, MaxQuant identified more than 6,000 proteins without matching to a library and with high quantitative reproducibility (R > 0.97). Online PASEF achieves a remarkable sensitivity with more than 2,500 proteins identified in 30 min runs of only 10 ng HeLa digest. We also show that highly reproducible collisional cross sections can be acquired on a large scale (R > 0.99). PASEF on the timsTOF Pro is a valuable addition to the technological toolbox in proteomics, with a number of unique operating modes that are only beginning to be explored. Fragmentation (computing) ; Mass spectrometry ; Ion ; Chemistry ; Hybrid mass spectrometer ; Computer science ; Selected reaction monitoring ; Tandem mass spectrometry ; Chromatography ; Operating system Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Yun Xu | Royston Goodacre
Source: Journal of Analysis and Testing (article, 2018-07-01)
Field: Chemistry — Analytical Chemistry

On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning Model validation is the most important part of building a supervised model. For building a model with good generalization performance one must have a sensible data splitting strategy, and this is crucial for model validation. In this study, we conducted a comparative study on various reported data splitting methods. The MixSim model was employed to generate nine simulated datasets with different probabilities of mis-classification and variable sample sizes. Then partial least squares for discriminant analysis and support vector machines for classification were applied to these datasets. Data splitting methods tested included variants of cross-validation, bootstrapping, bootstrapped Latin partition, Kennard-Stone algorithm (K-S) and sample set partitioning based on joint <i>X</i>-<i>Y</i> distances algorithm (SPXY). These methods were employed to split the data into training and validation sets. The estimated generalization performances from the validation sets were then compared with the ones obtained from the blind test sets which were generated from the same distribution but were unseen by the training/validation procedure used in model construction. The results showed that the size of the data is the deciding factor for the qualities of the generalization performance estimated from the validation set. We found that there was a significant gap between the performance estimated from the validation set and the one from the test set for the all the data splitting methods employed on small datasets. Such disparity decreased when more samples were available for training/validation, and this is because the models were then moving towards approximations of the central limit theory for the simulated datasets used. We also found that having too many or too few samples in the training set had a negative effect on the estimated model performance, suggesting that it is necessary to have a good balance between the sizes of training set and validation set to have a reliable estimation of model performance. We also found that systematic sampling method such as K-S and SPXY generally had very poor estimation of the model performance, most likely due to the fact that they are designed to take the most representative samples first and thus left a rather poorly representative sample set for model performance estimation. Cross-validation ; Generalization ; Bootstrapping (finance) ; Test set ; Sample size determination ; Computer science ; Data set ; Set (abstract data type) ; Artificial intelligence ; Linear discriminant analysis ; Sample (material) ; Data mining ; Mathematics ; Machine learning ; Statistics ; Pattern recognition (psychology) ; Econometrics Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Water Quality Monitoring and Analysis
Authors: Logan Ward | Alexander Dunn | Alireza Faghaninia | Nils Zimmermann | Saurabh Bajaj | Qi Wang | Joseph H. Montoya | Jiming Chen | Kyle Bystrom | Maxwell Dylla | Kyle Chard | Mark Asta | Kristin A. Persson | G. Jeffrey Snyder | Ian Foster | Anubhav Jain
Source: Computational Materials Science (article, 2018-05-25)
Field: Materials Science — Materials Chemistry

Matminer: An open source toolkit for materials data mining Python (programming language) ; Computer science ; Implementation ; Software ; Data mining ; Visualization ; Scope (computer science) ; Open source ; Data visualization ; Data structure ; Data science ; Software engineering ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Ruedi Aebersold | Jeffrey N. Agar | I. Jonathan Amster | Mark S. Baker | Carolyn R. Bertozzi | Emily S. Boja | Catherine E. Costello | Benjamin F. Cravatt | Catherine Fenselau | Benjamin A. García | Ying Ge | Jeremy Gunawardena | Ronald C. Hendrickson | Paul J. Hergenrother | Christian G. Huber | Alexander R. Ivanov | Ole N. Jensen | Michael C. Jewett | Neil L. Kelleher | Laura L. Kiessling | Nevan J. Krogan | Martin R. Larsen | Joseph A. Loo | Rachel R. Ogorzalek Loo | Emma Lundberg | Michael J. MacCoss | Parag Mallick | Vamsi K. Mootha | Milan Mrksich | Tom W. Muir | Steven M. Patrie | James J. Pesavento | Sharon J. Pitteri | Henry Rodriguez | Alan Saghatelian | Wendy Sandoval | Hartmut Schlüter | Salvatore Sechi | Sarah A. Slavoff | Lloyd M. Smith | M Snyder | Paul M. Thomas | Mathias Uhlén | Jennifer E. Van Eyk | Marc Vidal | David R. Walt | Forest M. White | Evan R. Williams | Therese Wohlschlager | Vicki H. Wysocki | Nathan A. Yates | Nicolas L. Young | Bing Zhang
Source: Nature Chemical Biology (article, 2018-02-14)
Field: Chemistry — Spectroscopy

How many human proteoforms are there? Human proteome project ; Proteome ; Computational biology ; Proteomics ; Function (biology) ; Biology ; Human genome ; Human disease ; Data science ; Computer science ; Gene ; Genome ; Genetics Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms ; Bioinformatics and Genomic Networks
Authors: Wei Ma | Feng Cheng | Yongmin Liu
Source: ACS Nano (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials Deep-learning framework has significantly impelled the development of modern machine learning technology by continuously pushing the limit of traditional recognition and processing of images, speech, and videos. In the meantime, it starts to penetrate other disciplines, such as biology, genetics, materials science, and physics. Here, we report a deep-learning-based model, comprising two bidirectional neural networks assembled by a partial stacking strategy, to automatically design and optimize three-dimensional chiral metamaterials with strong chiroptical responses at predesignated wavelengths. The model can help to discover the intricate, nonintuitive relationship between a metamaterial structure and its optical responses from a number of training examples, which circumvents the time-consuming, case-by-case numerical simulations in conventional metamaterial designs. This approach not only realizes the forward prediction of optical performance much more accurately and efficiently but also enables one to inversely retrieve designs from given requirements. Our results demonstrate that such a data-driven model can be applied as a very powerful tool in studying complicated light-matter interactions and accelerating the on-demand design of nanophotonic devices, systems, and architectures for real world applications. Metamaterial ; Computer science ; Nanophotonics ; Deep learning ; Stacking ; Artificial intelligence ; Limit (mathematics) ; Artificial neural network ; Computer architecture ; Nanotechnology ; Physics ; Materials science ; Optoelectronics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Neural Networks and Reservoir Computing
Authors: Hiroshi Kageyama | Katsuro Hayashi | Kazuhiko Maeda | J. Paul Attfield | Zenji Hiroi | James M. Rondinelli | Kenneth R. Poeppelmeier
Source: Nature Communications (review, 2018-02-16)
Field: Chemistry — Inorganic Chemistry

Expanding frontiers in materials chemistry and physics with multiple anions During the last century, inorganic oxide compounds laid foundations for materials synthesis, characterization, and technology translation by adding new functions into devices previously dominated by main-group element semiconductor compounds. Today, compounds with multiple anions beyond the single-oxide ion, such as oxyhalides and oxyhydrides, offer a new materials platform from which superior functionality may arise. Here we review the recent progress, status, and future prospects and challenges facing the development and deployment of mixed-anion compounds, focusing mainly on oxide-derived materials. We devote attention to the crucial roles that multiple anions play during synthesis, characterization, and in the physical properties of these materials. We discuss the opportunities enabled by recent advances in synthetic approaches for design of both local and overall structure, state-of-the-art characterization techniques to distinguish unique structural and chemical states, and chemical/physical properties emerging from the synergy of multiple anions for catalysis, energy conversion, and electronic materials. Nanotechnology ; Chemistry ; Physics ; Materials science Inorganic Chemistry and Materials ; Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics
Authors: Mirco Bundschuh | Juliane Filser | Simon Lüderwald | Moira S. McKee | George Metreveli | Gabriele E. Schaumann | Ralf Schulz | Stephan Wagner
Source: Environmental Sciences Europe (review, 2018-01-31)
Field: Materials Science — Materials Chemistry

Nanoparticles in the environment: where do we come from, where do we go to? Nanoparticles serve various industrial and domestic purposes which is reflected in their steadily increasing production volume. This economic success comes along with their presence in the environment and the risk of potentially adverse effects in natural systems. Over the last decade, substantial progress regarding the understanding of sources, fate, and effects of nanoparticles has been made. Predictions of environmental concentrations based on modelling approaches could recently be confirmed by measured concentrations in the field. Nonetheless, analytical techniques are, as covered elsewhere, still under development to more efficiently and reliably characterize and quantify nanoparticles, as well as to detect them in complex environmental matrixes. Simultaneously, the effects of nanoparticles on aquatic and terrestrial systems have received increasing attention. While the debate on the relevance of nanoparticle-released metal ions for their toxicity is still ongoing, it is a re-occurring phenomenon that inert nanoparticles are able to interact with biota through physical pathways such as biological surface coating. This among others interferes with the growth and behaviour of exposed organisms. Moreover, co-occurring contaminants interact with nanoparticles. There is multiple evidence suggesting nanoparticles as a sink for organic and inorganic co-contaminants. On the other hand, in the presence of nanoparticles, repeatedly an elevated effect on the test species induced by the co-contaminants has been reported. In this paper, we highlight recent achievements in the field of nano-ecotoxicology in both aquatic and terrestrial systems but also refer to substantial gaps that require further attention in the future. Nanoparticle ; Ecotoxicology ; Environmental chemistry ; Environmental science ; Contamination ; Aquatic environment ; Biochemical engineering ; Nanotechnology ; Chemistry ; Ecology ; Biology ; Materials science Nanoparticles: synthesis and applications ; Heavy metals in environment
Authors: Muhammad Jaleed Khan | Hamid Saeed Khan | Adeel Yousaf | Khurram Khurshid | Asad Abbas
Source: IEEE Access (review, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Modern Trends in Hyperspectral Image Analysis: A Review Over the past three decades, significant developments have been made in hyperspectral imaging due to which it has emerged as an effective tool in numerous civil, environmental, and military applications. Modern sensor technologies are capable of covering large surfaces of earth with exceptional spatial, spectral, and temporal resolutions. Due to these features, hyperspectral imaging has been effectively used in numerous remote sensing applications requiring estimation of physical parameters of many complex surfaces and identification of visually similar materials having fine spectral signatures. In the recent years, ground based hyperspectral imaging has gained immense interest in the research on electronic imaging for food inspection, forensic science, medical surgery and diagnosis, and military applications. This review focuses on the fundamentals of hyperspectral image analysis and its modern applications such as food quality and safety assessment, medical diagnosis and image guided surgery, forensic document examination, defense and homeland security, remote sensing applications such as precision agriculture and water resource management and material identification and mapping of artworks. Moreover, recent research on the use of hyperspectral imaging for examination of forgery detection in questioned documents, aided by deep learning, is also presented. This review can be a useful baseline for future research in hyperspectral image analysis. Hyperspectral imaging ; Computer science ; Remote sensing ; Identification (biology) ; Medical imaging ; Remote sensing application ; Artificial intelligence ; Data science ; Geography Spectroscopy and Chemometric Analyses ; Currency Recognition and Detection ; Remote-Sensing Image Classification
Authors: Bogdan Budnik | Ezra Levy | Guillaume Harmange | Nikolai Slavov
Source: Genome biology (article, 2018-10-09)
Field: Chemistry — Spectroscopy

SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation Some exciting biological questions require quantifying thousands of proteins in single cells. To achieve this goal, we develop Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) and validate its ability to identify distinct human cancer cell types based on their proteomes. We use SCoPE-MS to quantify over a thousand proteins in differentiating mouse embryonic stem cells. The single-cell proteomes enable us to deconstruct cell populations and infer protein abundance relationships. Comparison between single-cell proteomes and transcriptomes indicates coordinated mRNA and protein covariation, yet many genes exhibit functionally concerted and distinct regulatory patterns at the mRNA and the protein level. Proteome ; Biology ; Proteomics ; Computational biology ; Transcriptome ; Embryonic stem cell ; Cell ; Single-cell analysis ; Cell fate determination ; Cell type ; Scope (computer science) ; Cell biology ; Gene ; Gene expression ; Genetics ; Transcription factor Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Single-cell and spatial transcriptomics
Authors: Justin S. Smith | Benjamin Nebgen | Nicholas Lubbers | Olexandr Isayev | Adrián E. Roitberg
Source: The Journal of Chemical Physics (article, 2018-05-22)
Field: Materials Science — Materials Chemistry

Less is more: Sampling chemical space with active learning The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor researched in detail. In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials. It is based on the concept of active learning (AL) via Query by Committee (QBC), which uses the disagreement between an ensemble of ML potentials to infer the reliability of the ensemble's prediction. QBC allows the presented AL algorithm to automatically sample regions of chemical space where the ML potential fails to accurately predict the potential energy. AL improves the overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test cases by mitigating human biases in deciding what new training data to use. AL also reduces the training set size to a fraction of the data required when using naive random sampling techniques. To provide validation of our AL approach, we develop the COmprehensive Machine-learning Potential (COMP6) benchmark (publicly available on GitHub) which contains a diverse set of organic molecules. Active learning-based ANI potentials outperform the original random sampled ANI-1 potential with only 10% of the data, while the final active learning-based model vastly outperforms ANI-1 on the COMP6 benchmark after training to only 25% of the data. Finally, we show that our proposed AL technique develops a universal ANI potential (ANI-1x) that provides accurate energy and force predictions on the entire COMP6 benchmark. This universal ML potential achieves a level of accuracy on par with the best ML potentials for single molecules or materials, while remaining applicable to the general class of organic molecules composed of the elements CHNO. Benchmark (surveying) ; Computer science ; Machine learning ; Artificial intelligence ; Chemical space ; Task (project management) ; Set (abstract data type) ; Sampling (signal processing) ; Fraction (chemistry) ; Process (computing) ; Ensemble learning ; Active learning (machine learning) ; Energy (signal processing) ; Training set ; Data mining ; Statistics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions