jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Uwe Möginger | Niels Marcussen | Ole N. Jensen
Source: Oncotarget (article, 2020-11-03)
Field: Chemistry — Spectroscopy

Histo-molecular differentiation of renal cancer subtypes by mass spectrometry imaging and rapid proteome profiling of formalin-fixed paraffin-embedded tumor tissue sections Pathology differentiation of renal cancer types is challenging due to tissue similarities or overlapping histological features of various tumor (sub) types. As assessment is often manually conducted outcomes can be prone to human error and therefore require high-level expertise and experience. Mass spectrometry can provide detailed histo-molecular information on tissue and is becoming increasingly popular in clinical settings. Spatially resolving technologies such as mass spectrometry imaging and quantitative microproteomics profiling in combination with machine learning approaches provide promising tools for automated tumor classification of clinical tissue sections. In this proof of concept study we used MALDI-MS imaging (MSI) and rapid LC-MS/MS-based microproteomics technologies (15 min/sample) to analyze formalin-fixed paraffin embedded (FFPE) tissue sections and classify renal oncocytoma (RO, <i>n</i> = 11), clear cell renal cell carcinoma (ccRCC, <i>n</i> = 12) and chromophobe renal cell carcinoma (ChRCC, <i>n</i> = 5). Both methods were able to distinguish ccRCC, RO and ChRCC in cross-validation experiments. MSI correctly classified 87% of the patients whereas the rapid LC-MS/MS-based microproteomics approach correctly classified 100% of the patients. This strategy involving MSI and rapid proteome profiling by LC-MS/MS reveals molecular features of tumor sections and enables cancer subtype classification. Mass spectrometry provides a promising complementary approach to current pathological technologies for precise digitized diagnosis of diseases. Medicine ; Mass spectrometry imaging ; Mass spectrometry ; Proteome ; Profiling (computer programming) ; Computational biology ; Medical physics ; Pathology ; Computer science ; Bioinformatics ; Chemistry ; Chromatography ; Biology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Vigneshwari Subramanian | Ekaterina L. Ratkova | David Scott Palmer | Ola Engkvist | Maxim V. Fedorov | Antonio Llinàs
Source: Journal of Chemical Information and Modeling (article, 2020-04-20)
Field: Materials Science — Materials Chemistry

Multisolvent Models for Solvation Free Energy Predictions Using 3D-RISM Hydration Thermodynamic Descriptors The potential to predict solvation free energies (SFEs) in any solvent using a machine learning (ML) model based on thermodynamic output, extracted exclusively from 3D-RISM simulations in water is investigated. The models on multiple solvents take into account both the solute and solvent description and offer the possibility to predict SFEs of any solute in any solvent with root mean squared errors less than 1 kcal/mol. Validations that involve exclusion of fractions or clusters of the solutes or solvents exemplify the model's capability to predict SFEs of novel solutes and solvents with diverse chemical profiles. In addition to being predictive, our models can identify the solute and solvent features that influence SFE predictions. Furthermore, using 3D-RISM hydration thermodynamic output to predict SFEs in any organic solvent reduces the need to run 3D-RISM simulations in all these solvents. Altogether, our multisolvent models for SFE predictions that take advantage of the solvation effects are expected to have an impact in the property prediction space. Solvation ; Thermodynamics ; Energy (signal processing) ; Statistical physics ; Chemistry ; Physics ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Joshua J. Brown | Alister J. Page
Source: The Journal of Chemical Physics (article, 2020-12-11)
Field: Materials Science — Materials Chemistry

The Hubbard-U correction and optical properties of d metal oxide photocatalysts We report a systematic investigation of individual and multisite Hubbard-U corrections for the electronic, structural, and optical properties of the metal titanate oxide d<sup>0</sup> photocatalysts SrTiO<sub>3</sub> and rutile/anatase TiO<sub>2</sub>. Accurate bandgaps for these materials can be reproduced with local density approximation and generalized gradient approximation exchange-correlation density functionals via a continuous series of empirically derived U<sup>d</sup> and U<sup>p</sup> combinations, which are relatively insensitive to the choice of functional. On the other hand, lattice parameters are much more sensitive to the choice of U<sup>d</sup> and U<sup>p</sup>, but in a systematic way that enables the U<sup>d</sup> and U<sup>p</sup> corrections to be used to qualitatively gauge the extent of self-interaction error in the electron density. Modest U<sup>d</sup> corrections (e.g., 4 eV-5 eV) yield the most reliable dielectric response functions for SrTiO<sub>3</sub> and are comparable to the range of U<sup>d</sup> values derived via linear response approaches. For r-TiO<sub>2</sub> and a-TiO<sub>2</sub>, however, the U<sup>d,p</sup> corrections that yield accurate bandgaps fail to accurately describe both the parallel and perpendicular components of the dielectric response function. Analysis of individual U<sup>d</sup> and U<sup>p</sup> corrections on the optical properties of SrTiO<sub>3</sub> suggests that the most consequential of the two individual corrections is U<sup>d</sup>, as it predominately determines the accuracy of the dominant excitation from O-2p to the Ti-3d t<sub>2g</sub>/e<sub>g</sub> orbitals. U<sup>p</sup>, on the other hand, can be used to shift the entire optical response uniformly to higher frequencies. These results will assist high-throughput and machine learning approaches to screening photoactive materials based on d<sup>0</sup> photocatalysts. Anatase ; Hybrid functional ; Materials science ; Dielectric ; Density functional theory ; Oxide ; Atomic orbital ; Yield (engineering) ; Local-density approximation ; Electron ; Physics ; Electronic structure ; Condensed matter physics ; Molecular physics ; Chemistry ; Quantum mechanics ; Thermodynamics ; Optoelectronics ; Photocatalysis Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science
Authors: Manuel Corral Valero | Pascal Raybaud
Source: Journal of Catalysis (article, 2020-09-17)
Field: Materials Science — Materials Chemistry

Computational chemistry approaches for the preparation of supported catalysts: Progress and challenges Chemistry ; Catalysis ; Adsorption ; Transition metal ; Coordination complex ; Surface modification ; Oxide ; Zeolite ; Nickel ; Inorganic chemistry ; Heterogeneous catalysis ; Organometallic chemistry ; Metal ; Nanotechnology ; Organic chemistry ; Physical chemistry ; Materials science Catalytic Processes in Materials Science ; Mesoporous Materials and Catalysis ; Catalysis and Oxidation Reactions
Authors: Clelia Dispenza | Daniela Giacomazza | Mats Jönsson
Source: Biomolecules (review, 2020-12-31)
Field: Materials Science — Polymers and Plastics

Micro- to Nanoscale Bio-Hybrid Hydrogels Engineered by Ionizing Radiation Bio-hybrid hydrogels consist of a water-swollen hydrophilic polymer network encapsulating or conjugating single biomolecules, or larger and more complex biological constructs like whole cells. By modulating at least one dimension of the hydrogel system at the micro- or nanoscale, the activity of the biological component can be extremely upgraded with clear advantages for the development of therapeutic or diagnostic micro- and nano-devices. Gamma or e-beam irradiation of polymers allow a good control of the chemistry at the micro-/nanoscale with minimal recourse to toxic reactants and solvents. Another potential advantage is to obtain simultaneous sterilization when the absorbed doses are within the sterilization dose range. This short review will highlight opportunities and challenges of the radiation technologies to produce bio-hybrid nanogels as delivery devices of therapeutic biomolecules to the target cells, tissues, and organs, and to create hydrogel patterns at the nano-length and micro-length scales on surfaces. Biomolecule ; Self-healing hydrogels ; Nanotechnology ; Nanoscopic scale ; Materials science ; Polymer ; Ionizing radiation ; Drug delivery ; Irradiation ; Polymer chemistry Polymer Nanocomposite Synthesis and Irradiation ; Hydrogels: synthesis, properties, applications ; Nanoplatforms for cancer theranostics
Authors: Athira Johnson | Jialing He | Fanbin Kong | Yi‐Cheng Huang | Sabu Thomas | Hong‐Ting Victor Lin | Zwe‐Ling Kong
Source: International Journal of Nanomedicine (article, 2020-06-01)
Field: Materials Science — Biomaterials

&lt;p&gt;Surfactin-Loaded ĸ-Carrageenan Oligosaccharides Entangled Cellulose Nanofibers as a Versatile Vehicle Against Periodontal Pathogens&lt;/p&gt; The study showed the successful extraction of CNF and modification with CO improved the physical parameters of the CNF. In addition, surfactin-loaded CO-CNF has potential antimicrobial activity against periodontal pathogens. The obtained biomaterial is economically valuable and has great potential for biomedical applications. Nanofiber ; Antimicrobial ; Cellulose ; Chemistry ; Streptococcus mutans ; Bacterial cellulose ; Materials science ; Nuclear chemistry ; Biochemistry ; Organic chemistry ; Bacteria ; Nanotechnology ; Biology Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Polysaccharides and Plant Cell Walls
Authors: Yuanqi Wang | Changsheng Zhang | Yi‐Lei Zhao | Rosalinda L. Zhao | K. N. Houk
Source: Biomolecules (article, 2020-05-26)
Field: Materials Science — Materials Chemistry

Understand the Specific Regio- and Enantioselectivity of Fluostatin Conjugation in the Post-Biosynthesis Fluostatins, benzofluorene-containing aromatic polyketides in the atypical angucycline family, conjugate into dimeric and even trimeric compounds in the post-biosynthesis. The formation of the C-C bond involves a non-enzymatic stereospecific coupling reaction. In this work, the unusual regio- and enantioselectivities were rationalized by density functional theory calculations with the M06-2X (SMD, water)/6-311 + G(d,p)//6-31G(d) method. These DFT calculations reproduce the lowest energy C1-(R)-C10'-(S) coupling pathway observed in a nonenzymatic reaction. Bonding of the reactive carbon atoms (C1 and C10') of the two reactant molecules maximizes the HOMO-LUMO interactions and Fukui function involving the highest occupied molecular orbital (HOMO) of nucleophile p-QM and lowest unoccupied molecular orbital (LUMO) of electrophile FST<sub>2</sub><sup>-</sup> anion. In particular, the significant π-π stacking interactions of the low-energy pre-reaction state are retained in the lowest energy pathway for C-C coupling. The distortion/interaction-activation strain analysis indicates that the transition state (<b>TScp</b><b>-I</b>) of the lowest energy pathway involves the highest stabilizing interactions and small distortion among all possible C-C coupling reactions. One of the two chiral centers generated in this step is lost upon aromatization of the phenol ring in the final difluostatin products. Thus, the π-π stacking interactions between the fluostatin 6-5-6 aromatic ring system play a critical role in the stereoselectivity of the nonenzymatic fluostatin conjugation. Biosynthesis ; Chemistry ; Stereochemistry ; Biochemistry ; Combinatorial chemistry ; Computational biology ; Biology ; Enzyme Enzyme Structure and Function ; Biochemical Acid Research Studies ; Synthesis and Characterization of Pyrroles
Authors: Alper Soyler | Sevil Çıkrıkcı | Çağrı Çavdaroğlu | Dylan Bouillaud | Jonathan Farjon | Patrick Giraudeau | Mecit Halil Öztop
Source: LWT (article, 2020-11-13)
Field: Chemistry — Analytical Chemistry

Multi-scale benchtop 1H NMR spectroscopy for milk analysis Context (archaeology) ; Lactose ; Nuclear magnetic resonance spectroscopy ; Hydrolysis ; Chemistry ; Spectroscopy ; Continuous flow ; Quality (philosophy) ; Process engineering ; Food science ; Biochemical engineering ; Engineering ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; NMR spectroscopy and applications ; Meat and Animal Product Quality
Authors: Shisheng Wang | Hongwen Zhu | Hu Zhou | Jingqiu Cheng | Hao Yang
Source: BMC Bioinformatics (article, 2020-10-07)
Field: Chemistry — Spectroscopy

MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks This study deciphers proteome profiling of raw mass spectrometry data and broadens the promising application of the classification and prediction of proteomics data from multi-tumor samples using deep learning methods. MSpectraAI also shows a better performance compared to the other classical machine learning approaches. Profiling (computer programming) ; Computer science ; Proteome ; Proteomics ; Mass spectrometry ; Artificial intelligence ; Raw data ; Artificial neural network ; Pattern recognition (psychology) ; Data mining ; Bioinformatics ; Chemistry ; Biology ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Aimen El Orche | Mustapha Bouatia | Siham Yanisse | Houda Labjar | Mouad Mouhsin | Mohamed Bouha | Mustapha Oubenali | Mohamed Mbarki
Source: Journal of Spectroscopy (article, 2020-06-20)
Field: Chemistry — Organic Chemistry

Evaluation of the Capability of Horizontal ATR-FTMIR and UV-Visible Spectroscopy in the Discrimination of Virgin Olive Oils from the Moroccan Region of Beni Mellal-Khenifra One of the most important challenges in the authentication of olive oil is the determination of the geographical origin of virgin olive oil. In this work, we evaluated the capacity of two spectroscopic techniques, UV-Visible and ATR-FTMIR, coupled with chemometric tools to determine the geographical origin of olive oils. These analytical approaches have been applied to samples that have been collected during the period of olive oil production, in the Moroccan region of Beni Mellal-Khenifra. To develop a rapid analysis tool capable of authenticating the geographical origin of virgin olive oils from five geographical areas of the Moroccan region of Beni Mellal-Khenifra, UV-Visible and ATR-FTMIR spectral data were processed by chemometric algorithms. PCA was applied on the spectral data set to represent the data in a very small space, and then discrimination methods were applied on the principal components synthesized by the PCA. The application of the PCA-LDA method on the spectral data of UV-Visible and ATR-FTMIR shows a good ability to classify olive oils according to their geographical origin with a percentage of correct classification that represents 90.24% and 85.87%, respectively, and the processing of the spectral data of UV-Visible and ATR-FTMIR by PCA-SVM allows differentiating correctly between five olive oils with a correct classification rate of 100% and 97.56, respectively. This study demonstrated the feasibility of UV-Visible and ATR-FTMIR fingerprinting (routine technique) for the geographical classification of olive oils in the Moroccan region of Beni Mellal-Khenifra. Such developed methods can be proposed as alternative and complementary methods to authenticate the geographical origin of virgin olive oil. Olive oil ; Principal component analysis ; Chemometrics ; Pattern recognition (psychology) ; Environmental science ; Remote sensing ; Artificial intelligence ; Chemistry ; Computer science ; Chromatography ; Food science ; Geography Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food
Authors: Anbing Zheng | Huihua Yang | Xipeng Pan | Lihui Yin | Yanchun Feng
Source: IEEE Access (article, 2020-12-28)
Field: Chemistry — Analytical Chemistry

On-Site Identification of Counterfeit Drugs Based on Near-Infrared Spectroscopy Siamese-Network Modeling Near-infrared spectroscopy (NIR) has become one of the most important methods for counterfeit drugs identification for its low cost, non-destructive, and on-site detection. However, it is often invalid for unknown samples beyond the scope of modeling samples, as well as it is not efficient in conditions of insufficient samples (insufficient number of samples within the class), unbalanced samples (large difference in the number of samples between classes), and sensitivity of identification results (different tolerance for different errors in application scenarios). To solve these problems, this paper proposes a general method for on-site identification of counterfeit drugs based on Siamese-network modeling with near-infrared spectroscopy, which especially constructs the train set and test set, learning the general knowledge of spectral differences to identify the different drugs by a costumed one-dimensional convolution neural network (1D-CNN), and finally answered the question of whether the on-site two spectra are pointed to the same drug. Based on experimental modeling samples of 1314 spectra, which are involved 9 drugs produced by 25 manufacturers, this paper has constructed and fully trained its model. Then, not known at the time of modeling, 4 drugs produced by 9 manufacturers are used for testing in the on-site application, and the accuracy rate amounts to 97.3%. For generalizing consideration, randomly divided into training and testing categories, the 32015 spectra of 135 drugs produced by 391 manufacturers in the spectral library are handled by the same processing. The generalization model is equally applicable, and the accuracy is above 97%. Compared with traditional binary classification identification methods such as SVM, PLS-DA, Auto-encoding, and one class (OC) threshold identification algorithms such as SVM-OC, SIMCA, conformity test (CT), the proposed method has the best identification ability for unknown samples in modeling. Counterfeit ; Identification (biology) ; Computer science ; Artificial intelligence ; Set (abstract data type) ; Generalization ; Counterfeit Drugs ; Convolution (computer science) ; Pattern recognition (psychology) ; Artificial neural network ; Test set ; Infrared spectroscopy ; Machine learning ; Data mining ; Mathematics ; Chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Ke Zhou | Zhiping Xu
Source: Physical Review Research (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Deciphering the nature of ion-graphene interaction Ion-specific interactions with materials are important for the design of ion-based energy and information devices, which are studied here with a focus on alkali-earth-metal and transition-metal ions in the context of carbon nanofluidics. Density functional theory calculations show that for the alkali-earth-metal ions, the nature of interaction is ionic, with charge transfer to and from graphene determined by the change in work function. In contrast, covalent bonding is identified for transition-metal ions adsorbed on graphene, showing hybridization near the Dirac point. The ion-graphene interaction is significantly stronger than van der Waals interactions and shows much higher contrast among the ions. We find that, surprisingly, these features are robust even with the presence of water solvation, where alkali-earth-metal ion-graphene interaction remains ionic with modified charge transfer, while covalent interaction with transition-metal ions turns into ionic. These understandings imply that the ion-wall interaction can be notably tuned by the external electric field, as verified by our calculations, and opens an avenue for the development of nanofluidics where the channel width is comparable with the range of ion-wall interaction. Graphene ; Ionic bonding ; Chemical physics ; Ion ; Nanofluidics ; Chemistry ; van der Waals force ; Context (archaeology) ; Alkali metal ; Materials science ; Nanotechnology ; Molecule ; Organic chemistry Graphene research and applications ; Nanopore and Nanochannel Transport Studies ; Electrostatics and Colloid Interactions
Authors: Rafiga C. Masmaliyeva | Kave H. Babai | Garib N. Murshudov
Source: Acta Crystallographica Section D Structural Biology (article, 2020-09-22)
Field: Materials Science — Materials Chemistry

Local and global analysis of macromolecular atomic displacement parameters This paper describes the global and local analysis of atomic displacement parameters (ADPs) of macromolecules in X-ray crystallography. The distribution of ADPs is shown to follow the shifted inverse-gamma distribution or a mixture of these distributions. The mixture parameters are estimated using the expectation-maximization algorithm. In addition, a method for the resolution- and individual ADP-dependent local analysis of neighbouring atoms has been designed. This method facilitates the detection of mismodelled atoms, heavy-metal atoms and disordered and/or incorrectly modelled ligands. Both global and local analyses can be used to detect errors in atomic models, thus helping in the (re)building, refinement and validation of macromolecular structures. This method can also serve as an additional validation tool during PDB deposition. Macromolecule ; Displacement (psychology) ; Inverse ; Maximization ; Distribution (mathematics) ; Statistical physics ; Biological system ; Expectation–maximization algorithm ; Resolution (logic) ; Algorithm ; Chemistry ; Computer science ; Computational physics ; Physics ; Mathematics ; Mathematical optimization ; Statistics ; Geometry ; Biology ; Maximum likelihood ; Artificial intelligence ; Mathematical analysis Enzyme Structure and Function ; X-ray Diffraction in Crystallography ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Emilie S. Henault | Maria H. Rasmussen | Jan H. Jensen
Source: ChemRxiv (preprint, 2020-04-21)
Field: Chemistry — Physical and Theoretical Chemistry

Chemical Space Exploration: How Genetic Algorithms Find the Needle in the Haystack We attempt to explain why search algorithms can find molecules with particular properties in an enormous chemical space (ca 1060 molecules) by considering only a tiny subset (typically 103−6 molecules). Using a very simple example, we show that the number of potential paths that the search algorithms can follow to the target is equally vast. Thus, the probability of randomly finding a molecule that is on one of these paths is quite high and from here a search algorithm can follow the path to the target molecule. A path is defined as a series of molecules that have some non-zero quantifiable similarity (score) with the target molecule and that are increasingly similar to the target molecule. The minimum path length from any point in chemical space to the target corresponds is on the order of 100 steps, where a step is the change of and atom- or bond-type. Thus, a perfect search algorithm should be able to locate a particular molecule in chemical space by screening on the order of 100s of molecules, provided the score changes incrementally. We show that the actual number for a genetic search algorithm is between 100 and several millions, and depending on the target property and its dependence on molecular changes, the molecular representation, and the number of solutions to the search problem. Chemical space ; Algorithm ; Path (computing) ; Representation (politics) ; Space (punctuation) ; Haystack ; Molecule ; Point (geometry) ; Simple (philosophy) ; Computer science ; Linear search ; Mathematics ; Chemistry ; Drug discovery ; Artificial intelligence History and advancements in chemistry ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography