jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrea Fossati | Chen Li | Peter Sykacek | Moritz Heusel | Fabian Frommelt | Federico Uliana | Mahmoud Hallal | Isabell Bludau | Capraz Tümay Klemens | Peng Xue | Anthony W. Purcell | Matthias Gstaiger | Ruedi Aebersold
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-05-07)
Field: Chemistry — Spectroscopy

Systematic protein complex profiling and differential analysis from co-fractionation mass spectrometry data Abstract Protein complexes, macro-molecular assemblies of two or more proteins, play vital roles in numerous cellular activities and collectively determine the cellular state. Despite the availability of a range of methods for analysing protein complexes, systematic analysis of complexes under multiple conditions has remained challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise, for instance in the combination of size exclusion chromatography (SEC) with accurate protein quantification by SWATH/DIA-MS. However, most approaches for interpreting co-fractionation datasets to yield complex composition, abundance and rearrangements between samples depend heavily on prior evidence. We introduce PCprophet, a computational framework to identify novel protein complexes from SEC-SWATH-MS data and to characterize their changes across different experimental conditions. We demonstrate accurate prediction of protein complexes (AUC &gt;0.99 and accuracy around 97%) via five-fold cross-validation on SEC-SWATH-MS data, show improved performance over state-of-the-art approaches on multiple annotated co-fractionation datasets, and describe a Bayesian approach to analyse altered protein-protein interactions across conditions. PCprophet is a generic computational tool consisting of modules for data pre-processing, hypothesis generation, machine-learning prediction, post-prediction processing, and differential analysis. It can be applied to any co-fractionation MS dataset, independent of separation or quantitative LC-MS workflow employed, and to support the detection and quantitative tracking of novel protein complexes and their physiological dynamics. Fractionation ; Computer science ; Mass spectrometry ; Workflow ; Quantitative proteomics ; Chemistry ; Proteome ; Biological system ; Profiling (computer programming) ; Data mining ; Computational biology ; Proteomics ; Chromatography ; Biology ; Biochemistry ; Database Mass Spectrometry Techniques and Applications ; Microbial Metabolic Engineering and Bioproduction ; Protein Structure and Dynamics
Authors: Sumit Mandal | Jane Batcheller | Guowen Song | Indu Bala Grover
Source: Textile Research Journal (article, 2020-08-12)
Field: Materials Science — Polymers and Plastics

Characterization and empirical analysis of hot water immersion with compression protective performance of fabrics used in firefighters’ clothing This study aims to investigate hot water immersion with compression protective performance of textile fabrics used in firefighters’ clothing. This study has two key objectives – firstly, to characterize the protective performance of fabrics under different types of hot water immersion with compression exposures; secondly, to empirically analyze the protective performance of these fabrics under different exposures. To accomplish both the objectives, the physical properties (e.g., thickness, air permeability) of multi-layered fabrics that are commonly used in firefighters’ clothing were measured. Next, the protective performances of these fabrics were evaluated under different exposures. The experimental data obtained were statistically analyzed to identify the effects of the fabrics’ physical properties on the performance. Also, the performances provided by the fabrics were compared, and the nature of heat and mass transfer through the fabrics was explored. Using the significant fabric properties that affected the performance, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) modeling techniques were used to empirically predict the performance of the fabrics. The best prediction models were then employed for saliency testing to understand the relative importance of the significant fabric properties on the performance. The study demonstrates that the protective performance of textile fabrics varies with the exposures, depending upon the mass transfer through fabrics. In these exposures, fabric thickness, air or water-vapor permeability, and evaporative resistance are found to be the primary properties to consider in protecting the wearer. In this study, it has been identified that ANN models can be effectively used in comparison to MLR models for predicting the protective performance. By analyzing the best-fit ANN models, it is identified that different fabric properties play a key role in predicting the protective performance. Overall, this study would enhance the understanding of fabric materials used in firefighters’ clothing. This deeper understanding could be applied to engineer new test standards and fabric materials for clothing to provide optimum occupational health and safety for firefighters. Air permeability specific surface ; Clothing ; Textile ; Materials science ; Composite material ; Immersion (mathematics) ; Mathematics Textile materials and evaluations ; Thermoregulation and physiological responses ; Color perception and design
Authors: Katherine Sharp | Heather R. Everson | Rebecca McNall Krall
Source: The FASEB Journal (article, 2020-04-01)
Field: Chemistry — Physical and Theoretical Chemistry

Examining Curricular Trends in Introductory Biochemistry Courses at Large Universities Current conversations in chemical education highlight the importance of student‐centered learning approaches and the need to implement such learning approaches in college classrooms. However, commentary and other conversations continue to suggest traditional lectures are predominantly used to teach biochemistry. The goal of this study was to determine curricular trends within introductory biochemistry courses at R1 institutions of higher education. By determining the current state of biochemistry education, strategies can be developed to promote student‐centered learning strategies, particularly in large lecture courses common in biochemistry at the introductory level. The primary focus of this study was to identify common teaching and learning strategies presented in course syllabi. Introductory‐level biochemistry syllabi were collected from 42 R1 institutions. All syllabi were approved by the American Chemical Society (ACS) or counted towards an ACS‐approved degree/program. Syllabi were assessed on course delivery methods, pedagogical strategies used within the course, forms of formative and summative assessment, activities to establish real‐world connections, and major topics presented to students. The results indicate that most courses were presented in a traditional lecture format with few integrating student‐centered strategies such as peer‐led team instruction. Similarly, few syllabi described instructional scaffolds used to construct real‐life connections with biochemistry content. Topics most frequently addressed in the courses included fundamental building blocks, biopolymers, and supramolecular structures. Not surprising was the ubiquitous use of large lecture‐style courses. Lecture has long been shown to be an efficient method of instruction that promote memorization of facts rather than cultivating complex understandings essential in problem‐solving. Results presented demonstrate the need for student‐centered strategies in biochemistry at R1 universities to promote deeper learning, develop critical thinking and problem‐solving skills, and foster connections between content and real‐world applications students could face in their future careers. Syllabus ; Summative assessment ; Formative assessment ; Mathematics education ; Curriculum ; Construct (python library) ; Memorization ; Higher education ; Chemistry ; Psychology ; Computer science ; Pedagogy ; Political science Various Chemistry Research Topics ; Biomedical and Engineering Education ; Genetics, Bioinformatics, and Biomedical Research
Authors: Daniel Parrish
Source: CERES (Cranfield University) (article, 2020-01-08)
Field: Chemistry — Analytical Chemistry

Improving the Performance of Nuclear Quadrupole Resonance Sensing Technologies for in situ Detection of Narcotics and Explosives &lt;div&gt;Nuclear Quadrupole Resonance (NQR) and Nuclear Magnetic Resonance (NMR) are spectroscopic techniques that offer the ability to characterise samples non-destructively in situ for quality control in industrial processes (e.g., water content in food), and for detection of explosives and narcotics in defence and security sensing applications. Current NMR-/ NQR-based sensing technologies can achieve good performance in a controlled laboratory environment where the effect of external Radio Frequency Interference (RFI) can be mitigated by RFI shielding. However, for in situ sensing applications outside the laboratory, complete physical shielding is often not possible or not practical (heavy, bulky) and therefore alternative methods are needed for NMR-/ NQR-based sensing technologies to be useful.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;This EngD project focusses on the development of methods for active elimination of RFI for in situ sensing applications of NMR and NQR. The work aims to develop signal processing techniques that work with varying degrees of physical suppression (RFI shielding), to improve the accuracy and reliability of the NMR-/ NQR-based sensing technologies. The approach being developed is a machine learning process in which RFI is automatically identified using a decision tree model followed by a RFI suppression algorithm to produce the RFI-minimised signal.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;This talk describes the development of an experimental testbed for data collection and some recent results achieved by applying the algorithms to measured NQR data subject to simulated burst mode RFI. The performance of the decision tree model is validated against human operator performance data, generated by volunteers rating the degree to which they could confidently declare a NQR signal present or not in RFI-polluted data. The performance of the model is quantified as a Receiver Operating Characteristic (ROC) curve, which plots true positive against false positive for a binary classifier. For the data described here, the decision tree model improved (relative to no RFI removed) the area under curve (AUC) value from 0.58 to 0.906, where AUC = 1 means a 100% detection rate with a 0% false alarm rate.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;This work contains material subject to @Crown Copyright (2019), Dstl.&lt;/i&gt;&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt; Electromagnetic shielding ; Nuclear quadrupole resonance ; Computer science ; Explosive material ; Materials science ; Nuclear magnetic resonance ; Engineering ; Physics ; Electrical engineering ; Chemistry Spectroscopy and Chemometric Analyses ; Solid-state spectroscopy and crystallography ; Advanced Chemical Sensor Technologies
Authors: Carmen Leitner | Marion Schulz | M.K. Tham | Maria V. Babak | Wei Ang | Dan Wang | Curt Wentrup | Voltaic Batteries | Paul T. Pfluger | Frank Glorius | Dwayne E. Heard | Anne Lennox | Tianxiao Ye | Zhaohui Zhong | Adolfo Garcı́a-Sastre | Michael Schotsaert | Bart De Geest | B Wang | M. Perea | Richmond Sarpong | Songyan Zheng | Jingwen Jiang | A Van Der Lee | Mihail Bãrboiu | Zhibing Zhang | S Peh | Y Wang | Kang Cheng | Fan Wang | Dandan Zhao | Chunya Ji | Dibyendu Dey | Yi Peng | X. Liu | Lei Li | Jie Luo | Jean‐François Rey | Charles Bignaud | Pascal Raybaud | T Buc | Céline Chizallet | Sudipta Maiti | Subal Chandra Manna | Nicholas Banahene | Lam Pham | Zhiqing Liang | Jinling Wang | Yincao Xu | Robert L. Bettinger | John Zientko | Aaron P. Esser‐Kahn | Wei Du | Sary Abou Derhamine | Tetiana Krachko | Nuno Monteiro | Guillaume Pilet | Johannes Schranck | Anwar Tlili | Abderrahmane Amgoune | Daibin Yang | Song Zuo | Hua Yang | Yi Zhou | X Wang | Chao Wang | Fei Jing | K Wang | Jinghua Li | Zhou Lan | Weizhong Yan | Xue‐Long Sun | Liuping Wang | Yi Tang | Vasily S. Minkov | V. Prakapenka | E Greenberg | M. I. Eremets | H. M. Hu | Xinyu Hu | Yuan Liu | Florian F. Mulks | Leonie J. Bole | Laurence Davin | A Hernμn-Gómez | Arthur G. Kennedy | J García-Lvarez | Eva Hevia | Xiaohua Jia | X Chen | Andrew Hong | Xinya Bu | Feng Pan | Ryuji Kyan | Koichi Sato | N Mase | Tetsuo Narumi | Kai Tao | Sigal Rencus‐Lazar | Yuxiang Yao | Bo Xue
Source: Angewandte Chemie International Edition (article, 2020-10-12)
Field: Chemistry — Organic Chemistry

Graphical Abstract: Angew. Chem. Int. Ed. 43/2020 A voltage-responsive synthetic Cl channel regulated by pH is demonstrated by M. Barboiu et al. in their Communication on page 18920. The dual voltage/pH regulated channel system represents an unexplored alternative for ion pumping along artificial ion channels. Citation ; Computer science ; INT ; Library science ; Information retrieval ; Programming language Click Chemistry and Applications ; Cyclopropane Reaction Mechanisms ; Fluorine in Organic Chemistry
Authors: Shi Jun Ang | Wujie Wang | Daniel Schwalbe‐Koda | Simon Axelrod | Rafael Gómez‐Bombarelli
Source: ChemRxiv (preprint, 2020-02-28)
Field: Materials Science — Materials Chemistry

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces &lt;div&gt;Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires &lt;i&gt;ab initio&lt;/i&gt; molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a dataset size of approximately&lt;/div&gt;&lt;div&gt;31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. Thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a minor role for the reaction in vacuum. Furthermore, a transfer-learning strategy effectively upgraded the potential energy surface to higher&lt;/div&gt;&lt;div&gt;levels of theory ((SMD-)M06-2X/def2-TZVPD in vacuum and three other solvents, as well as the more accurate DLPNO-DSD-PBEP86 D3BJ/def2-TZVPD) using about 10% additional calculations for each surface. Since the larger basis set and the dynamic correlation capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for the charge-separated intermediate on the more accurate potential energy surfaces. The character of the intermediate switches from entropic to thermodynamic upon including implicit solvation effects, with lifetimes increasing with solvent polarity. Analysis of 2,000 reactive trajectories on the chloroform PES shows a qualitative agreement with the experimentally-reported periselectivity for this reaction. This overall approach is broadly applicable and opens a door to the study of dynamical effects in larger, previously-intractable reactive systems.&lt;/div&gt; Ab initio ; Potential energy surface ; Molecular dynamics ; Chemistry ; Transition state theory ; Intramolecular force ; Transition state ; Chemical physics ; Physics ; Computational chemistry ; Statistical physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular Junctions and Nanostructures
Authors: Chenru Duan | Fang Liu | Aditya Nandy | Heather J. Kulik
Source: ChemRxiv (preprint, 2020-04-13)
Field: Materials Science — Materials Chemistry

Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics High-throughput computational screening typically employs methods (i.e., density functional theory or DFT) that can fail to describe challenging molecules, such as those with strongly correlated electronic structure. In such cases, multireference (MR) correlated wavefunction theory (WFT) would be the appropriate choice but remains more challenging to carry out and automate than single-reference (SR) WFT or DFT. Numerous diagnostics have been proposed for identifying when MR character is likely to have an effect on the predictive power of SR calculations, but conflicting conclusions about diagnostic performance have been reached on small data sets. We compute 15 MR diagnostics, ranging from affordable DFT-based to more costly MR-WFT-based diagnostics, on a set of 3,165 equilibrium and distorted small organic molecules containing up to six heavy atoms. Conflicting MR character assignments and low pairwise linear correlations among diagnostics are also observed over this set. We evaluate the ability of existing diagnostics to predict the percent recovery of the correlation energy, %&lt;i&gt;E&lt;/i&gt;&lt;sub&gt;corr&lt;/sub&gt;. None of the DFT-based diagnostics are nearly as predictive of %&lt;i&gt;E&lt;/i&gt;&lt;sub&gt;corr&lt;/sub&gt; as the best WFT-based diagnostics. To overcome the limitation of this cost–accuracy trade-off, we develop machine learning (ML, i.e., kernel ridge regression) models to predict WFT-based diagnostics from a combination of DFT-based diagnostics and a new, size-independent 3D geometric representation. The ML-predicted diagnostics correlate as well with MR effects as their computed (i.e., with WFT) values, significantly improving over the DFT-based diagnostics on which the models were trained. These ML models thus provide a promising approach to improve upon DFT-based diagnostic accuracy while remaining suitably low cost for high-throughput screening. Pairwise comparison ; Computer science ; Density functional theory ; Algorithm ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Martín Leandro Paleico | Jörg Behler
Source: The Journal of Chemical Physics (preprint, 2020-08-04)
Field: Materials Science — Materials Chemistry

Global optimization of copper clusters at the ZnO(101¯0) surface using a DFT-based neural network potential and genetic algorithms The determination of the most stable structures of metal clusters supported at solid surfaces by computer simulations represents a formidable challenge due to the complexity of the potential-energy surface. Here, we combine a high-dimensional neural network potential, which allows us to predict the energies and forces of a large number of structures with first-principles accuracy, with a global optimization scheme employing genetic algorithms. This very efficient setup is used to identify the global minima and low-energy local minima for a series of copper clusters containing between four and ten atoms adsorbed at the ZnO(101¯0) surface. A series of structures with common structural features resembling the Cu(111) and Cu(110) surfaces at the metal-oxide interface has been identified, and the geometries of the emerging clusters are characterized in detail. We demonstrate that the frequently employed approximation of a frozen substrate surface in global optimization can result in missing the most relevant structures. Maxima and minima ; Global optimization ; Artificial neural network ; Energy minimization ; Copper ; Surface (topology) ; Potential energy surface ; Series (stratigraphy) ; Genetic algorithm ; Materials science ; Adsorption ; Algorithm ; Oxide ; Metal ; Potential energy ; Biological system ; Computer science ; Chemical physics ; Computational chemistry ; Chemistry ; Molecule ; Mathematics ; Physics ; Geometry ; Artificial intelligence ; Physical chemistry ; Atomic physics ; Machine learning ; Mathematical analysis ZnO doping and properties ; Machine Learning in Materials Science ; Surface and Thin Film Phenomena
Authors: M. M. Montemore | Chukwudi F. Nwaokorie | Gbolade O. Kayode
Source: ChemRxiv (preprint, 2020-03-17)
Field: Materials Science — Materials Chemistry

General Screening of Surface Alloys for Catalysis Intensive research in catalysis has resulted in design parameters for many important catalytic reactions; however, designing new catalysts remains difficult, partly due to the time and expense needed to screen a large number of potential catalytic surfaces. Here, we create a general, efficient model that can be used to screen surface alloys for many reactions without any quantum-based calculations. This model allows the prediction of the adsorption energies of a variety of species (explicitly shown for C, N, O, OH, H, S, K, F) on metal alloy surfaces that include combinations of nearly all of the d-block metals. We find that a few simple structural features, chosen using data-driven techniques and physical understanding, can be used to predict electronic structure properties. These electronic structure properties are then used to predict adsorption energies, which are in turn used to predict catalytic performance. This framework is interpretable and gives insight into how underlying structural features affect adsorption and catalytic performance. We apply the model to screen more than 107 unique surface sites on approximately 106 unique surfaces for 7 important reactions. We identify novel surfaces with high predicted catalytic performance, and demonstrate challenges and opportunities in catalyst development using surface alloys. This work shows the utility of a general, reusable model that can be applied in new contexts without requiring new data to be generated. Catalysis ; Adsorption ; Surface (topology) ; Variety (cybernetics) ; Alloy ; Metal ; Materials science ; Chemical physics ; Electronic structure ; Work (physics) ; Heterogeneous catalysis ; Nanotechnology ; Computer science ; Chemistry ; Computational chemistry ; Thermodynamics ; Physical chemistry ; Physics ; Mathematics ; Metallurgy ; Organic chemistry ; Artificial intelligence Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science