jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Iseult Lynch | Penny Nymark | Philip Doganis | Mary Gulumian | Tae Hyun Yoon | Diego Stéfani T. Martinez | Antreas Afantitis
Source: F1000Research (preprint, 2021-11-24)
Field: Materials Science — Materials Chemistry

Methods, models, mechanisms and metadata: Introducing the Nanotoxicology collection at F1000Research Nanotoxicology is a relatively new field of research concerning the study and application of nanomaterials to evaluate the potential for harmful effects in parallel with the development of applications. Nanotoxicology as a field spans materials synthesis and characterisation, assessment of fate and behaviour, exposure science, toxicology / ecotoxicology, molecular biology and toxicogenomics, epidemiology, safe and sustainable by design approaches, and chemoinformatics and nanoinformatics, thus requiring scientists to work collaboratively, often outside their core expertise area. This interdisciplinarity can lead to challenges in terms of interpretation and reporting, and calls for a platform for sharing of best-practice in nanotoxicology research. The F1000Research Nanotoxicology collection, introduced via this editorial, will provide a place to share accumulated best practice, via original research reports including no-effects studies, protocols and methods papers, software reports and living systematic reviews, which can be updated as new knowledge emerges or as the domain of applicability of the method, model or software is expanded. This editorial introduces the Nanotoxicology Collection in <i>F1000Research</i>. The aim of the collection is to provide an open access platform for nanotoxicology researchers, to support an improved culture of data sharing and documentation of evolving protocols, biological and computational models, software tools and datasets, that can be applied and built upon to develop predictive models and move towards <i>in silico</i> nanotoxicology and nanoinformatics. Submissions will be assessed for fit to the collection and subjected to the F1000Research open peer review process. Nanotoxicology ; Toxicogenomics ; Data science ; Computer science ; Engineering ; Biology Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods
Authors: Daniil Kazantsev | Ramona Duman | Armin Wagner | Vitaliy Mykhaylyk | Kazimir Wanelik | Mark Basham | Nicola Wadeson
Source: Journal of Synchrotron Radiation (article, 2021-04-20)
Field: Materials Science — Materials Chemistry

X-ray tomographic reconstruction and segmentation pipeline for the long-wavelength macromolecular crystallography beamline at Diamond Light Source In this paper a practical solution for the reconstruction and segmentation of low-contrast X-ray tomographic data of protein crystals from the long-wavelength macromolecular crystallography beamline I23 at Diamond Light Source is provided. The resulting segmented data will provide the path lengths through both diffracting and non-diffracting materials as basis for analytical absorption corrections for X-ray diffraction data taken in the same sample environment ahead of the tomography experiment. X-ray tomography data from protein crystals can be difficult to analyse due to very low or absent contrast between the different materials: the crystal, the sample holder and the surrounding mother liquor. The proposed data processing pipeline consists of two major sequential operations: model-based iterative reconstruction to improve contrast and minimize the influence of noise and artefacts, followed by segmentation. The segmentation aims to partition the reconstructed data into four phases: the crystal, mother liquor, loop and vacuum. In this study three different semi-automated segmentation methods are experimented with by using Gaussian mixture models, geodesic distance thresholding and a novel morphological method, RegionGrow, implemented specifically for the task. The complete reconstruction-segmentation pipeline is integrated into the MPI-based data analysis and reconstruction framework Savu, which is used to reduce computation time through parallelization across a computing cluster and makes the developed methods easily accessible. Segmentation ; Computer science ; Beamline ; Optics ; Tomographic reconstruction ; Computation ; Pipeline (software) ; Computer vision ; Artificial intelligence ; Algorithm ; Iterative reconstruction ; Physics ; Beam (structure) Enzyme Structure and Function ; Advanced X-ray Imaging Techniques ; Medical Imaging Techniques and Applications
Authors: Kai Liu | Ralf Nilsson | Elisa Lázaro‐Ibáñez | Tasso Miliotis | Michael Lerche | Ana Rita Salgado Ribeiro | Marie Strimfors | Hanna Duàn | Johan Ulander | Daniel Lindén | Anna Salvati | Alan Sabirsh
Source: Research Square (preprint, 2021-09-14)
Field: Materials Science — Biomaterials

An obesity model and corona multiomics analysis reveal high-density lipoprotein effects on lipid nanoparticle function Function (biology) ; Nanoparticle ; Corona (planetary geology) ; High-density lipoprotein ; Physics ; Chemistry ; Materials science ; Nanotechnology ; Biology ; Cell biology ; Cholesterol ; Astrobiology ; Biochemistry Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Advanced Nanomaterials in Catalysis
Authors: Ying Zhang | Min Zou | Adil Farooq Lodhi | Yulin Deng
Source: AMB Express (article, 2021-12-01)
Field: Materials Science — Biomaterials

Microbial degradation and community structure analysis of hydroxyl-terminated polybutadiene (HTPB) Hydroxyl-terminated polybutadiene (HTPB) is a curing adhesive that is commonly used in the production of ammunition, and it emerged during the time of war. After entering the peaceful era, several countries around the globe have focused on the destruction of expired ammunition using safe and economical methods in terms of consumption of energy. Microorganisms exhibit a highly efficient and environment friendly degradation capability for variety of refractory substances. Therefore, in this study we screened five strains of microorganisms from five environmental soil samples for their ability to degrade HTPB. These microorganisms were identified as Microbacterium trichothecenolyticum, Microbacterium esteraromaticum, Arthrobacter pascens, Pseudonocardia carboxydivorans and Ochrobactrum anthropic based on 16S rRNA gene similarity index. We observed the uncorroded and corroded HTPB sample through scanning electron microscopy and observed the formation of lot of holes and gullies in HTPB after corrosion. An 18S rRNA gene clone library was constructed for HTPB-degrading fungi. Based on the results of library evaluation, it was found that the structure of the HTPB-degrading fungi community was relatively simple. A total of 54 positive clones were obtained. These clones represented some uncultured microorganisms that were closely related to Scytalidium lignicola, Pseudokahliella and Gonostomum strenuum. This study will help in the implementation of environment friendly degradation strategies for HTPB degradation. Microorganism ; Microbacterium ; 16S ribosomal RNA ; Environmentally friendly ; Degradation (telecommunications) ; Bioremediation ; Chemistry ; Materials science ; Environmental chemistry ; Microbiology ; Biology ; Bacteria ; Gene ; Biochemistry ; Ecology biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Antimicrobial agents and applications
Authors: Thi Hue Tran | Quốc Toản Trần | Thi Thao Ta | Si-Hung Le
Source: E3S Web of Conferences (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Geographical origin identification of teas using UV-VIS spectroscopy In this work we proposed a method to verify the differentiating characteristics of simple tea infusions prepared in boiling water alone, which represents the final product as ingested by the consumers. For this purpose, total of 125 tea samples from different geographical provines of Vietnam have been analyzed in UV-Vis spectroscopy associated with multivariate statistical methods. Principal Component Analysis-Discriminant Analysis (PCA-DA), Partial Least Squares Discriminant Analysis (PLS-DA) and Artificial Neural Network (ANN) were compared to construct the identification model. The experimental results showed that the performance of ANN model was better than PCA-DA and PLS-DA model. The optimal ANN model was achieved when neuron numbers were 200, identification rate being 99% in the training set and 84% predition set. The proposed methodology provides a simpler, faster and more affordable classification of simple tea infusions, and can be used as an alternative approach to traditional tea quality evaluation. Principal component analysis ; Linear discriminant analysis ; Partial least squares regression ; Artificial neural network ; Multivariate statistics ; Pattern recognition (psychology) ; Identification (biology) ; Mathematics ; Artificial intelligence ; Chemometrics ; Statistics ; Biological system ; Computer science ; Machine learning ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Raissa Oliveira Rocha Alves | Otávio Chedid Tomé | Pollyanna Cardoso Pereira | Camila Nair Batista Couto Villanoeva | Vanelle Maria da Silva
Source: Ciência Rural (article, 2021-10-11)
Field: Chemistry — Analytical Chemistry

Artificial neural networks in the prediction of fraud in integral milk powder by adding whey powder ABSTRACT: This research was performed to ascertain the most suitable Artificial Neural Network (ANN) model to quantify the degree of fraud in powdered milk through the addition of powdered whey via regular standard physicochemical analyses. In this study, an evaluation was done on 103 samples with different quantities of added whey powder to whole milk powder. Using Fourier Transform Infrared Spectroscopy the fat, cryoscopy, total solids, defatted dry extract, lactose, protein and casein were analyzed. The hyperbolic tangent transformation function was used with 45 topologies, and the Holdback and K-fold validation methods were tested. In the Holdback method, 75% of the database was employed for training, while 25% was used for validation. In the K-fold method, the database was categorized into five equal sized subsets, which alternated between training and validation. Of the two methods, the K-fold method was proven to have superior efficiency. Next, analysis was done on three models of multilayer perceptron networks with feedforward architecture. In Model 1, the input layer contained all the physicochemical analyses conducted, in model 2 the casein analysis was excluded, and in model 3 the routine analyses performed for dairy products was done (fat, defatted dry extract, cryoscopy and total solids). From Model 3 an ANN was derived which could satisfactorily predict fraud calculated from using the routine and standard analyses for dairy products, containing 64 nodes in the hidden layer, with R2 of 0.9935 and RMSE of 0.5779 for training, and R2 of 0.9964 and RMSE of 0.4358 for validation. Lactose ; Casein ; Artificial neural network ; Mathematics ; Food science ; Chemistry ; Chromatography ; Machine learning ; Computer science Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Meat and Animal Product Quality
Authors: Santiago Ruiz | L.A. Sarabia | M.S. Sánchez | M.C. Ortiz
Source: Frontiers in Chemistry (article, 2021-07-13)
Field: Chemistry — Analytical Chemistry

Handling Variables, via Inversion of Partial Least Squares Models for Class-Modelling, to Bring Defective Items to Non-Defective Ones In the context of binary class-modelling techniques, the paper presents the computation in the input space of linear boundaries of a class-model constructed with given values of sensitivity and specificity. This is done by inversion of a decision threshold, set with these values of sensitivity and specificity, in the probabilistic class-models computed by means of PLS-CM (Partial Least Squares for Class-Modelling). The characterization of the boundary hyperplanes, in the latent space (space spanned by the selected latent variables of the fitted PLS model) or in the input space, makes it possible to calculate directions that can be followed to move objects toward the class-model of interest. Different points computed along these directions will show how to modify the input variables (provided they can be manipulated) so that, eventually, a computed 'object' would be inside the class-model, in terms of the prediction with the PLS model. When the class of interest is that of "adequate" objects, as for example in some process control or product formulation, the proposed procedure helps in answering the question about how to modify the input variables so that a defective object would be inside the class-model of the adequate (non-defective) ones. This is the situation illustrated with some examples, taken from the literature when modelling the class of adequate objects. Hyperplane ; Class (philosophy) ; Sensitivity (control systems) ; Boundary (topology) ; Mathematics ; Computer science ; Probabilistic logic ; Binary number ; Partial least squares regression ; Latent class model ; Context (archaeology) ; Set (abstract data type) ; Algorithm ; Artificial intelligence ; Machine learning ; Combinatorics ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Statistical Process Monitoring
Authors: Simone Ciarella | Chengjie Luo | Vincent E. Debets | Liesbeth M. C. Janssen
Source: The European Physical Journal E (preprint, 2021-07-01)
Field: Chemistry — Physical and Theoretical Chemistry

Multi-component generalized mode-coupling theory: predicting dynamics from structure in glassy mixtures The emergence of glassy dynamics and the glass transition in dense disordered systems is still not fully understood theoretically. Mode-coupling theory (MCT) has shown to be effective in describing some of the non-trivial features of glass formation, but it cannot explain the full glassy phenomenology due to the strong approximations on which it is based. Generalized mode-coupling theory (GMCT) is a hierarchical extension of the theory, which is able to outclass MCT by carefully describing the dynamics of higher-order correlations in its generalized framework. Unfortunately, the theory has so far only been developed for single-component systems and as a result works poorly for highly polydisperse materials. In this paper, we solve this problem by developing GMCT for multi-component systems. We use it to predict the glassy dynamics of the binary Kob-Andersen Lennard-Jones mixture, as well as its purely repulsive Weeks-Chandler-Andersen analogue. Our results show that each additional level of the GMCT hierarchy gradually improves the predictive power of GMCT beyond its previous limit. This implies that our theory is able to harvest more information from the static correlations, thus being able to better understand the role of attraction in supercooled liquids from a first-principles perspective. Statistical physics ; Supercooling ; Mode coupling ; Component (thermodynamics) ; Coupling (piping) ; Fragility ; Phenomenology (philosophy) ; Glass transition ; Hierarchy ; Binary number ; Physics ; Mathematics ; Materials science ; Thermodynamics ; Quantum mechanics Advanced Physical and Chemical Molecular Interactions ; Theoretical and Computational Physics ; Material Dynamics and Properties
Authors: Carter Rhea | Laurie Rousseau-Nepton | S. Prunet | Julie Hlavacek-Larrondo | René Pierre Martin | Kathryn Grasha | N. Vale Asari | Théophile Bégin | Benjamin Vigneron | Myriam Prasow-Émond
Source: The Astrophysical Journal (article, 2021-12-01)
Field: Chemistry — Analytical Chemistry

A Machine-learning Approach to Integral Field Unit Spectroscopy Observations. III. Disentangling Multiple Components in H ii Regions Abstract In the first two papers of this series, we demonstrated the dynamism of machine learning applied to optical spectral analysis by using neural networks to extract kinematic parameters and emission-line ratios directly from the spectra observed by the SITELLE instrument located at the Canada–France–Hawai’i Telescope. In this third installment, we develop a framework using a convolutional neural network trained on synthetic spectra to determine the number of line-of-sight components present in the SN3 filter (656–683 nm) spectral range of SITELLE. We compare this methodology to standard practice using Bayesian inference. Our results demonstrate that a neural network approach returns more accurate results and uses fewer computational resources over a range of spectral resolutions. Furthermore, we apply the network to SITELLE observations of the merging galaxy system NGC 2207/IC 2163. We find that the closest interacting sector and the central regions of the galaxies are best characterized by two line-of-sight components while the outskirts and spiral arms are well-constrained by a single component. Determining the number of resolvable components is crucial in disentangling different galactic components in merging systems and properly extracting their respective kinematics. Artificial intelligence ; Artificial neural network ; Convolutional neural network ; Computer science ; Spectral line ; Filter (signal processing) ; Pattern recognition (psychology) ; Component (thermodynamics) ; Line (geometry) ; Kinematics ; Telescope ; Inference ; Physics ; Segmentation ; Galaxy ; Astrophysics ; Computer vision ; Mathematics ; Astronomy ; Geometry Spectroscopy and Chemometric Analyses ; Astronomy and Astrophysical Research ; Stellar, planetary, and galactic studies
Authors: Atakan Başkor | Yağmur Pirinçci Tok | Burcu Mesut | Yıldız Özsoy | Tamer Uçar
Source: Healthcare Informatics Research (article, 2021-10-31)
Field: Chemistry — Analytical Chemistry

Estimating the Optimal Dexketoprofen Pharmaceutical Formulation with Machine Learning Methods and Statistical Approaches In this study, we developed a computational approach to estimate the optimal pharmaceutical formulation of dexketoprofen. The results were evaluated by an expert, and it was found that they complied with Food and Drug Administration criteria. Support vector machine ; Gradient boosting ; Random forest ; Machine learning ; Food and drug administration ; Mathematics ; Python (programming language) ; Linear regression ; Artificial intelligence ; Boosting (machine learning) ; Computer science ; Statistics ; Medicine Analytical Methods in Pharmaceuticals ; Drug Solubulity and Delivery Systems ; Computational Drug Discovery Methods
Authors: Laura De Clerck | Sander Willems | Simon Daled | Bart Van Puyvelde | Sigrid Verhelst | Laura Corveleyn | Dieter Deforce | Maarten Dhaenens
Source: Molecular Omics (article, 2021-09-13)
Field: Chemistry — Spectroscopy

An experimental design to extract more information from MS-based histone studies Histone-based chromatin organization paved the way for eukaryotic genome complexity. Because of their key role in information management, the histone posttranslational modifications (hPTM), which mediate their function, have evolved into an alphabet that has more letters than there are amino acids, together making up the "histone code". The resulting combinatorial complexity is manifold higher than what is usually encountered in proteomics. Consequently, a considerably bigger part of the acquired MSMS spectra remains unannotated to date. Adapted search parameters can dig deeper into the dark histone ion space, but the lack of false discovery rate (FDR) control and the high level of ambiguity when searching combinatorial PTMs makes it very hard to assess whether the newly assigned ions are informative. Therefore, we propose an easily adoptable time-lapse enzymatic deacetylation (HDAC1) of a commercial histone extract as a quantify-first strategy that allows isolating ion populations of interest, when studying <i>e.g.</i> acetylation on histones, that currently remain in the dark. By adapting search parameters to study potential issues in sample preparation, data acquisition and data analysis, we stepwise managed to double the portion of annotated precursors of interest from 10.5% to 21.6%. This strategy is intended to make up for the lack of validated FDR control and has led to several adaptations of our current workflow that will reduce the portion of the dark histone ion space in the future. Finally, this strategy can be applied with any enzyme targeting a modification of interest. Histone ; Computational biology ; Computer science ; Chemistry ; Biochemistry ; Biology ; DNA Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: José Ramón Macías | Rubén Sánchez-García | Pablo Conesa | Erney Ramírez-Aportela | Marta Martínez | Carlos Wert-Carvajal | Alberto M. Parra‐Perez | Joan Segura Mora | Sam Horrell | Andrea Thorn | Carlos Óscar S. Sorzano | J.M. Carazo
Source: Bioinformatics (article, 2021-05-19)
Field: Materials Science — Materials Chemistry

3DBionotes COVID-19 edition Supplementary data are available at Bioinformatics online. Coronavirus disease 2019 (COVID-19) ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; 2019-20 coronavirus outbreak ; Virology ; Computer science ; Betacoronavirus ; Computational biology ; Library science ; Medicine ; Biology ; Internal medicine ; Outbreak Enzyme Structure and Function ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Morten N. Gjerding | Thorbjørn Skovhus | Asbjørn Rasmussen | Fabian Bertoldo | Ask Hjorth Larsen | Jens Jørgen Mortensen | Kristian S. Thygesen
Source: Computational Materials Science (preprint, 2021-08-05)
Field: Materials Science — Materials Chemistry

Atomic Simulation Recipes: A Python framework and library for automated workflows The Atomic Simulation Recipes (ASR) is an open source Python framework for working with atomistic materials simulations in an efficient and sustainable way that is ideally suited for high-throughput projects. Central to ASR is the concept of a Recipe: a high-level Python script that performs a well defined simulation task robustly and accurately while keeping track of the data provenance. The ASR leverages the functionality of the Atomic Simulation Environment (ASE) to interface with external simulation codes and attain a high abstraction level. We provide a library of Recipes for common simulation tasks employing density functional theory and many-body perturbation schemes. These Recipes utilize the GPAW electronic structure code, but may be adapted to other simulation codes with an ASE interface. Being independent objects with automatic data provenance control, Recipes can be freely combined through Python scripting giving maximal freedom for users to build advanced workflows. ASR also implements a command line interface that can be used to run Recipes and inspect results. The ASR Migration module helps users maintain their data while the Database and App modules makes it possible to create local databases and present them as customized web pages. Python (programming language) ; Scripting language ; Computer science ; Workflow ; Programming language ; Source code ; Database Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Data Storage Technologies