jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: William H. Green
Source: Faraday Discussions (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Concluding remarks: <i>Faraday Discussion</i> on unimolecular reactions This <i>Faraday Discussion</i>, marking the centenary of Lindemann's explanation of the pressure-dependence of unimolecular reactions, presented recent advances in measuring and computing collisional energy transfer efficiencies, microcanonical rate coefficients, and pressure-dependent (phenomenological) rate coefficients, and the incorporation of these rate coefficients in kinetic models. Several of the presentations featured systems where breakdown of the Born-Oppenheimer approximation is key to understanding the measured rates/products. Many of the reaction systems presented were quite complex, which can make it difficult to go from "plausible proposed explanation" to "quantitative agreement between model and experiment". This complexity highlights the need for better automation of the calculations, better documentation and benchmarking to catch any errors and to make the calculations more easily reproducible, and continued (and even closer) cooperation of experimentalists and modelers. In some situations the correct definition of a "species" is debatable, since the population distributions and time evolution are so distorted from the perfect-Boltzmann Lewis-structure zero-order concept of a chemical species. Despite all these challenges, the field has made tremendous advances, and several cases were presented which demonstrated both excellent understanding of very complicated reaction chemistry and quantitatively accurate predictions of complicated experiments. Some of the interesting contributions to this <i>Discussion</i> are highlighted here, with some comments and suggestions for next steps. Faraday cage ; Physics ; Quantum mechanics Molecular spectroscopy and chirality ; History and advancements in chemistry ; Spectroscopy and Quantum Chemical Studies
Authors: Siyang Hu | Cheng Xiang-ai | Weibao He | Yuze Hu | Mingyu Tong | Zhongjie Xu
Source: Chinese Optics Letters (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inversely-designed terahertz metadevice with ultrafast modulation of double electromagnetically induced transparency windows Terahertz metasurfaces have great applications for efficient terahertz modulation, but there are still problems in designing terahertz metadevices in terms of complexity and inefficiency. Herein, we demonstrate an inversely-designed terahertz metasurface with double electromagnetically induced transparency (EIT)-like windows by incorporating a particle swarm optimization (PSO) algorithm with the finite-difference time-domain method. We prepared and tested the metadevices, and the experimental terahertz signals are close to the designed results. By hybridizing amorphous germanium film with the inversely-designed metasurface, two EIT-like windows, including transmission and slow-light effect, exhibit ultrafast modulation behavior in 25 ps excited by a femtosecond laser. The modulation depths of transmission in two transparency windows are 74% and 65%, respectively. The numerical simulations also illustrate the ultrafast dynamic process and modulation mechanism, which match well with the experiment results. Our work thus offers opportunities for designing other objective functions of the terahertz metadevice. Terahertz radiation ; Ultrashort pulse ; Optics ; Electromagnetically induced transparency ; Femtosecond ; Modulation (music) ; Terahertz spectroscopy and technology ; Optoelectronics ; Materials science ; Photomixing ; Laser ; Physics ; Far-infrared laser ; Terahertz metamaterials Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Plasmonic and Surface Plasmon Research
Authors: Valentyna Zinchenko | Johannes Hugger | Virginie Uhlmann | Detlev Arendt | Anna Kreshuk
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-05-07)
Field: Materials Science — Surfaces, Coatings and Films

MorphoFeatures: unsupervised exploration of cell types, tissues and organs in volume electron microscopy Abstract Electron microscopy (EM) provides a uniquely detailed view of cellular morphology, including organelles and fine subcellular ultrastructure. While the acquisition and (semi-)automatic segmentation of multicellular EM volumes is now becoming routine, large-scale analysis remains severely limited by the lack of generally applicable pipelines for automatic extraction of comprehensive morphological descriptors. Here, we present a novel unsupervised method for learning cellular morphology features directly from 3D EM data: a convolutional neural network delivers a representation of cells by shape and ultrastructure. Applied to the full volume of an entire three-segmented worm of the annelid Platynereis dumerilii , it yields a visually consistent grouping of cells supported by specific gene expression profiles. Integration of features across spatial neighbours can retrieve tissues and organs, revealing, for example, a detailed organization of the animal foregut. We envision that the unbiased nature of the proposed morphological descriptors will enable rapid exploration of very different biological questions in large EM volumes, greatly increasing the impact of these invaluable, but costly resources. Multicellular organism ; Segmentation ; Artificial intelligence ; Computer science ; Annelid ; Organelle ; Ultrastructure ; Convolutional neural network ; Foregut ; Biology ; Identification (biology) ; Representation (politics) ; Volume (thermodynamics) ; Pattern recognition (psychology) ; Biological system ; Anatomy ; Cell ; Cell biology ; Physics ; Ecology Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Zachary C. Drake | Justin T. Seffernick | Steffen Lindert
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-04-30)
Field: Chemistry — Spectroscopy

Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling Abstract Structural mass spectrometry offers several techniques for the characterization of protein structures. Covalent labeling (CL) in combination with mass spectrometry can be used as an analytical tool to study and determine structural properties of protein-protein complexes. Degrees of modification obtained from CL experiments for specific labeled residues can be compared between the unbound and bound states of complexes. This analysis can yield insights into structural features of these protein assemblies, specifically the proximity of specific residues to the protein-protein interface. However, this data is sparse and does not unambiguously elucidate protein structure. Thus, computational algorithms are needed to deduce structure from the CL data. In this work we present a novel hybrid method that combines models of protein complex subunits generated with AlphaFold with differential CL data via a CL-guided protein-protein docking in Rosetta. In a benchmark set, the RMSD (root-mean-square deviation) of the best-scoring models was below 3.6 Å for 5/5 complexes with inclusion of CL data, whereas the same quality was only achieved for 1/5 complexes without CL data. The average improvement in RMSD observed upon inclusion of CL data was 5.2 Å. This study suggests that our integrated approach can successfully use data obtained from CL experiments to distinguish between nativelike and non-nativelike models. Significance Statement Structural mass spectrometry can be a powerful and versatile approach to characterize the structure of protein complexes. Data obtained from covalent labeling mass spectrometry can provide insights into higher order protein structure (particularly with respect to residue interactions and solvent accessibility) but needs to be supplemented by computational techniques to elucidate accurate, atomic-detail structural information. Here, we present a method to combine bioanalytical data obtained from covalent labeling with models generated using AlphaFold to accurately predict protein-protein complexes in Rosetta. Differential covalent labeling data can be used to determine the proximity of residues to the binding interface of complexes which we utilized to analyze computational models and improve structure prediction algorithms. Mass spectrometry ; Chemistry ; Protein structure ; Protein Data Bank ; Protein structure prediction ; Covalent bond ; Proteomics ; Biological system ; Chromatography ; Biochemistry ; Biology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Computational Drug Discovery Methods
Authors: Yan Huo | Xin Li | Binbin Tu
Source: Crystals (article, 2022-11-22)
Field: Materials Science — Materials Chemistry

Image Measurement of Crystal Size Growth during Cooling Crystallization Using High-Speed Imaging and a U-Net Network In this paper, an image measurement method using a high-speed imaging system is proposed for the evolution of crystal population sizes during cooling crystallization processes. Firstly, to resist the negative effect from solution stirring and particle motion during crystallization, a U-net network-based image processing method is established to efficiently detect sufficiently clear crystals from the online captured microscopic images. Accordingly, the crystal size distribution model is analyzed in terms of the counted probability densities of these crystal images. Subsequently, a measurement method of size growth rate based on crystal population distribution is proposed to estimate the growth condition. An experimental case on a crystallization process of β-form LGA is used to show the effectiveness of the proposed strategy. Crystallization ; Crystal (programming language) ; Materials science ; Image (mathematics) ; Process (computing) ; Crystal growth ; Population ; Particle (ecology) ; Particle size ; Particle-size distribution ; Biological system ; Net (polyhedron) ; Artificial intelligence ; Computer science ; Crystallography ; Mathematics ; Physics ; Geometry ; Chemistry ; Engineering ; Thermodynamics ; Chemical engineering Crystallization and Solubility Studies ; Image Processing Techniques and Applications ; Industrial Vision Systems and Defect Detection
Authors: Muhammad Nasir Amin | Izaz Ahmad | Asim Abbas | Kaffayatullah Khan | Muhammad Ghulam Qadir | Mudassir Iqbal | Abdullah Mohammad Abu-Arab | Anas Abdulalim Alabdullah
Source: Materials (article, 2022-08-26)
Field: Materials Science — Materials Chemistry

Estimating Radiation Shielding of Fired Clay Bricks Using ANN and GEP Approaches This study aimed to determine how radiation attenuation would change when the thickness, density, and compressive strength of clay bricks, modified with partial replacement of clay by fly ash, iron slag, and wood ash. To conduct this investigation, four distinct types of bricks-normal, fly ash-, iron slag-, and wood ash-incorporated bricks were prepared by replacing clay content with their variable percentages. Additionally, models for predicting the radiation-shielding ability of bricks were created using gene expression programming (GEP) and artificial neural networks (ANN). The addition of iron slag improved the density and compressive strength of bricks, thus increasing shielding capability against gamma radiation. In contrast, fly ash and wood ash decreased the density and compressive strength of burnt clay bricks, leading to low radiation shielding capability. Concerning the performance of the Artificial Intelligence models, the root mean square error (RMSE) was determined as 0.1166 and 0.1876 nC for the training and validation data of ANN, respectively. The training set values for the GEP model manifested an RMSE equal to 0.2949 nC, whereas the validation data produced RMSE = 0.3507 nC. According to the statistical analysis, the generated models showed strong concordance between experimental and projected findings. The ANN model, in contrast, outperformed the GEP model in terms of accuracy, producing the lowest values of RMSE. Moreover, the variables contributing towards shielding characteristics of bricks were studied using parametric and sensitivity analyses, which showed that the thickness and density of bricks are the most influential parameters. In addition, the mathematical equation generated from the GEP model denotes its significance such that it can be used to estimate the radiation shielding of burnt clay bricks in the future with ease. Electromagnetic shielding ; Radiation shielding ; Materials science ; Environmental science ; Geotechnical engineering ; Composite material ; Geology Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Nuclear Physics and Applications
Authors: Berit Zeller‐Plumhoff | Ashwini Rahul Akkineni | Heike Helmholz | Dmytro Orlov | Maria Moßhammer | Michael Kühl | Regine Willumeit‐Römer | Michael Gelinsky
Source: npj Materials Degradation (article, 2022-11-18)
Field: Materials Science — Biomaterials

Oxygen-sensitive nanoparticles reveal the spatiotemporal dynamics of oxygen reduction during magnesium implant biodegradation Abstract Magnesium (Mg) alloys are becoming increasingly important in the biomedical field as temporary bone implants. However, the biodegradation process of Mg alloys is highly complex and recent findings suggest that oxygen (O 2 ) consumption is non-negligible. In this study, we give experimental proof of O 2 consumption during Mg degradation under physiological conditions. Specifically, we study pure Mg, Mg–6 wt%Ag and Mg–5 wt%Gd in Hanks’ balanced salt solution and Dulbecco’s modified Eagle’s medium. We show that O 2 consumption and hydrogen evolution are inversely correlated and that O 2 concentrations remain below 7.5% in certain cases, which could have significant implications for bone healing. Magnesium ; Oxygen ; Biodegradation ; Salt (chemistry) ; Chemistry ; Nanoparticle ; Degradation (telecommunications) ; Chemical engineering ; Nuclear chemistry ; Materials science ; Nanotechnology ; Organic chemistry Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Aluminum Alloys Composites Properties
Authors: Yali Zhang | Luchao Bai | Yuan Qi | Huasheng Huang | Xiaoyang Lu | Junqi Xiao | Yubin Lan | Muhua Lin | Jizhong Deng
Source: Agriculture (article, 2022-05-25)
Field: Chemistry — Analytical Chemistry

Detection of Rice Spikelet Flowering for Hybrid Rice Seed Production Using Hyperspectral Technique and Machine Learning Effective detection of rice spikelet flowering is crucial to the determination of optimal pollination timing for hybrid rice seed production. Currently, the detection of rice spikelet flowering status relies on manual observation of farmers, which has low efficiency and large errors. This study attempts to acquire rice spikelet flowering information using a hyperspectral technique and machine learning in order to meet the needs of hybrid rice seed pollination rapidly and automatically. Hyperspectral data of rice male parents with flowering and non-flowering in two experimental sites were collected with an ASD FieldSpec® HandHeld™2 spectrometer. Three traditional classifiers, Random Forest (RF), Support Vector Machine (SVM) and Back Propagation (BP) neural network, and Convolutional Neural Network (CNN), were used to build classification models for rice spikelets flowering detection. Three data processing methods, PCA feature extraction, GA feature selection, and the PCA and GA combination algorithm, were used for data dimensionality reduction. By comparing the precision and recall rate of different algorithms and data processing methods, the algorithms applicable to identify rice spikelet flowering were investigated. Results show that by evaluating different feature reduction methods and classifiers, the optimal model for rice spikelets flowering detection is the BP model with PCA feature extraction. The accuracy of the model reaches up to 96–100%. Hyperspectral technology and machine learning algorithm are capable of effective detection of rice spikelet flowering. This study provides technical reference for accurate judgment of rice flowering and helps to determine the optimal operation time for supplementary pollination of hybrid rice. Hyperspectral imaging ; Support vector machine ; Convolutional neural network ; Pollination ; Artificial intelligence ; Dimensionality reduction ; Hybrid seed ; Artificial neural network ; Computer science ; Mathematics ; Agronomy ; Machine learning ; Pattern recognition (psychology) ; Biology ; Botany ; Hybrid Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; GABA and Rice Research
Authors: Bader E Alabkary
Source: Orthopedics and Rheumatology Open Access Journal (article, 2022-07-11)
Field: Materials Science — Materials Chemistry

Review of Synovial Fluid Properties and Measurement Arthritis is an inflammation in one or more of the body joints.Its chief symptoms are pain and stiffness in the affected area.The accompanying pain in knee joints occurs due to stiffening joint fluids which are known as synovial fluids.Its kinematics identifies the compressive load and articular cartilage characteristics.This paper highlights synovial fluid properties and explores the lubrication mechanisms of synovial fluid for age-related changes while accounting for gender differences.Further, this paper reviews extant studies that were conducted on automated detection based on deep learning and acoustic monitoring of joint health and response to measurement by new methods such as electromechanical impedance (EMI) of a piezoelectric torsional transducer. Medicine ; Synovial fluid ; Pathology ; Alternative medicine Metal Alloys Wear and Properties ; Tribology and Lubrication Engineering ; Powder Metallurgy Techniques and Materials
Authors: Tomi Suomi | Laura L. Elo
Source: Immunology Letters (article, 2022-04-02)
Field: Chemistry — Spectroscopy

Statistical and machine learning methods to study human CD4+ T cell proteome profiles Mass spectrometry proteomics has become an important part of modern immunology, making major contributions to understanding protein expression levels, subcellular localizations, posttranslational modifications, and interactions in various immune cell populations. New developments in both experimental and computational techniques offer increasing opportunities for exploring the immune system and the molecular mechanisms involved in immune responses. Here, we focus on current computational approaches to infer relevant information from large mass spectrometry based protein profiling datasets, covering the different steps of the analysis from protein identification and quantification to further mining and modelling of the protein abundance data. Additionally, we provide a summary of the key proteome profiling studies on human CD4<sup>+</sup> T cells and their different subtypes in health and disease. Proteome ; Proteomics ; Computational biology ; Profiling (computer programming) ; Immune system ; Human proteome project ; Biology ; Quantitative proteomics ; Identification (biology) ; Mass spectrometry ; Bioinformatics ; Computer science ; Data science ; Chemistry ; Immunology ; Biochemistry Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Advanced Biosensing Techniques and Applications
Authors: Jian Zhang | Jin Yuan | Chuanzhen Li | Bin Li
Source: Electronics (article, 2022-06-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An Inverse Design Framework for Isotropic Metasurfaces Based on Representation Learning A hybrid framework for solving the non-uniqueness problem in the inverse design of isomorphic metasurfaces is proposed. The framework consists of a representation learning (RL) module and a variational autoencoder-particle swarm optimization (VAE-PSO) algorithm module. The RL module is used to reduce the complex high-dimensional space into a low-dimensional space with obvious features, with the purpose of eliminating the many-to-one relationship between the original design space and response space. The VAE-PSO algorithm first encodes all meta-atoms into a continuous latent space through VAE and then applies PSO to search for an optimized latent vector whose corresponding metasurface fulfills the target response. This framework gives the solution paradigm of the ideal non-uniqueness situation, simplifies the complexity of the network, improves the running speed of the PSO algorithm, and obtains the global optimal solution with 94% accuracy on the test set. Autoencoder ; Particle swarm optimization ; Uniqueness ; Representation (politics) ; Ideal (ethics) ; Inverse ; Algorithm ; Space (punctuation) ; Set (abstract data type) ; Computer science ; Isotropy ; Mathematics ; Mathematical optimization ; Artificial neural network ; Artificial intelligence ; Mathematical analysis ; Geometry ; Physics Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Animal Vocal Communication and Behavior
Authors: Anna Maria Dąbrowska | Agnieszka Adamczyk‐Woźniak | Izabela D. Madura
Source: CrystEngComm (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Effect of substituents in novel bioactive tavaborole derivatives on the intermolecular interaction hierarchy Tavaborole, a molecule based on the benzoxaborole scaffold, is an effective antifungal drug marketed under the Kerydin® trademark. Intermolecular force ; Antifungal ; Chemistry ; Combinatorial chemistry ; Molecule ; Scaffold ; Intermolecular interaction ; Hierarchy ; Stereochemistry ; Organic chemistry ; Computer science ; Biology Organoboron and organosilicon chemistry ; Asymmetric Synthesis and Catalysis ; Synthetic Organic Chemistry Methods
Authors: Jianhong Zhang | Min Wang | Keming Yang | Bing Wu | Yaxing Li | Yanru Li | Qianqian Han
Source: IEEE Access (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Predicting Copper and Lead Concentration in Crops Using Reflectance Spectroscopy Associated With Intrinsic Wavelength-Scale Decomposition Spectral Transformation Hyperspectral remote sensing is a reliable solution for monitoring heavy metal pollution in crops. However, there are few studies on the spectral transformation and estimation of heavy metal content in crops using time-frequency analysis. In this study, intrinsic wavelength-scale decomposition (IWD) was proposed to decompose hyperspectral data to fully exploit the sensitive information implied in them and to investigate the feasibility of the detection of copper (Cu) and lead (Pb) in maize leaves. Leaf spectra and Cu2+ and Pb2+ contents were obtained from potted maize plants under Cu and Pb stress in the laboratory. After the spectral data were processed using IWD to obtain the proper rotation components (PRCi), the characteristic bands were extracted, a Hankel matrix was constructed, and singular value decomposition (SVD) was performed. Finally, singular entropy information was obtained to characterize the heavy metal content. Singular entropy, with a higher correlation with Cu2+ and Pb2+ contents, was selected to establish the univariate and multivariate partial least squares regression (PLSR) models. The results showed the following: (1) the R2 of the univariate model for the prediction of copper and lead content was 0.68~0.81, and the RMSE was 0.99~7.03. (2)The R2 of the multivariate PLSR model was as high as 0.83, and the RMSE was as high as 0.83. This study showed that the characteristic bands can be effectively extracted by IWD spectral transformation, which provides a promising method for estimating heavy metal pollution in vegetation. Hyperspectral imaging ; Partial least squares regression ; Biplot ; Univariate ; Singular value decomposition ; Multivariate statistics ; Principal component analysis ; Entropy (arrow of time) ; Biological system ; Transformation (genetics) ; Mathematics ; Environmental science ; Remote sensing ; Chemistry ; Statistics ; Physics ; Algorithm Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Remote Sensing in Agriculture