jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. Ruzza | Giuseppe Lodato | Giovanni Rosotti
Source: Astronomy and Astrophysics (article, 2024-02-13)
Field: Chemistry — Spectroscopy

DBNets: A publicly available deep learning tool to measure the masses of young planets in dusty protoplanetary discs Current methods to characterize embedded planets in protoplanetary disc observations are severely limited either in their ability to fully account for the observed complex physics or in their computational and time costs. To address this shortcoming, we developed DBNets: a deep learning tool, based on convolutional neural networks, that analyses substructures observed in the dust continuum emission of protoplanetary discs to quickly infer the mass of allegedly embedded planets. We focussed on developing a method to reliably quantify not only the planet mass, but also the associated uncertainty introduced by our modelling and adopted techniques. Our tests gave promising results achieving an 87% reduction of the log M p mean squared error with respect to an analytical formula fitted on the same data (DBNets metrics: lmse 0.016, r2-score 97%). With the goal of providing the final user of DBNets with all the tools needed to interpret their measurements and decide on their significance, we extensively tested our tool on out-of-distribution data. We found that DBNets can identify inputs strongly outside its training scope returning an uncertainty above a specific threshold and we thus provided a rejection criterion that helps determine the significance of the results obtained. Additionally, we outlined some limitations of our tool: it can be reliably applied only on discs observed with inclinations below approximately 60°, in the optically thin regime, with a resolution ~8 times better than the gap radial location and with a signal-to-noise ratio higher than approximately ten. Finally, we applied DBNets to 33 actual observations of protoplanetary discs measuring the mass of 48 proposed planets and comparing our results with the available literature. We confirmed that most of the observed gaps imply planets in the sub-Jupiter regime. Planet ; Convolutional neural network ; Physics ; Measure (data warehouse) ; Astrophysics ; Computer science ; Artificial intelligence ; Algorithm ; Data mining Spectroscopy and Laser Applications ; Astrophysics and Star Formation Studies ; Scientific Measurement and Uncertainty Evaluation
Authors: Anita Dornes | Lisa Schmidt | Christopher-Nils Mais | John C. Hook | Jan Pané‐Farré | Dieter Kressler | Kai M. Thormann | Gert Bange
Source: Nature Communications (article, 2024-07-10)
Field: Materials Science — Materials Chemistry

Polar confinement of a macromolecular machine by an SRP-type GTPase The basal structure of the bacterial flagellum includes a membrane embedded MS-ring (formed by multiple copies of FliF) and a cytoplasmic C-ring (composed of proteins FliG, FliM and FliN). The SRP-type GTPase FlhF is required for directing the initial flagellar protein FliF to the cell pole, but the mechanisms are unclear. Here, we show that FlhF anchors developing flagellar structures to the polar landmark protein HubP/FimV, thereby restricting their formation to the cell pole. Specifically, the GTPase domain of FlhF interacts with HubP, while a structured domain at the N-terminus of FlhF binds to FliG. FlhF-bound FliG subsequently engages with the MS-ring protein FliF. Thus, the interaction of FlhF with HubP and FliG recruits a FliF-FliG complex to the cell pole. In addition, the modulation of FlhF activity by the MinD-type ATPase FlhG controls the interaction of FliG with FliM-FliN, thereby regulating the progression of flagellar assembly at the pole. Macromolecule ; Polar ; GTPase ; Physics ; Chemistry ; Nanotechnology ; Biophysics ; Biology ; Cell biology ; Materials science ; Biochemistry Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Microtubule and mitosis dynamics
Authors: Adel H. Elmetwalli | A. Derbala | Ibtisam Mohammed Alsudays | Eman A. Al-Shahari | Mahmoud Elhosary | Salah Elsayed | Laila A. Al‐Shuraym | Farahat S. Moghanm | Osama Elsherbiny
Source: PLoS ONE (article, 2024-08-26)
Field: Chemistry — Analytical Chemistry

Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits. Titratable acid ; Mandarin Chinese ; Hyperspectral imaging ; RGB color model ; Lycopene ; Chlorophyll ; Mathematics ; Reflectivity ; Ripening ; Postharvest ; Horticulture ; Artificial intelligence ; Carotenoid ; Botany ; Biology ; Computer science ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Joachim Sødequist | Thomas Olsen
Source: npj Computational Materials (article, 2024-08-04)
Field: Materials Science — Materials Chemistry

Magnetic order in the computational 2D materials database (C2DB) from high throughput spin spiral calculations Abstract We report high throughput computational screening for magnetic ground state order in 2D materials. The workflow is based on spin spiral calculations and yields the magnetic order in terms of a two-dimensional ordering vector Q . We then include spin-orbit coupling to extract the easy and hard axes for collinear structures and the orientation of spiral planes in non-collinear structures. Finally, for all predicted ferromagnets we compute the Dzyaloshinskii-Moriya interactions and determine whether or not these are strong enough to overcome the magnetic anisotropy and stabilise a chiral spin spiral ground state. We find 58 ferromagnets, 21 collinear anti-ferromagnets, and 85 non-collinear ground states of which 15 are chiral spin spirals driven by Dzyaloshinskii-Moriya interactions. The results show that non-collinear order is in fact as common as collinear order in these materials and emphasise the need for detailed investigation of the magnetic ground state when reporting magnetic properties of new materials. Spiral (railway) ; Throughput ; Order (exchange) ; Spin (aerodynamics) ; Condensed matter physics ; Materials science ; Computer science ; Physics ; Engineering ; Mechanical engineering ; Telecommunications ; Thermodynamics ; Business Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; 2D Materials and Applications
Authors: Joe D. Morrow | Chinonso Ugwumadu | D. A. Drabold | Stephen R. Elliott | Andrew L. Goodwin | Volker L. Deringer
Source: Angewandte Chemie International Edition (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

Understanding Defects in Amorphous Silicon with Million‐Atom Simulations and Machine Learning The structure of amorphous silicon (a-Si) is widely thought of as a fourfold-connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such "dangling-bond" and "floating-bond" defects, respectively, a unified understanding is still missing. Here, we use advanced computational chemistry methods to reveal the complex structural and energetic landscape of defects in a-Si. We study an ultra-large-scale, quantum-accurate structural model containing a million atoms, and thousands of individual defects, allowing reliable defect-related statistics to be obtained. We combine structural descriptors and machine-learned atomic energies to develop a classification of the different types of defects in a-Si. The results suggest a revision of the established floating-bond model by showing that fivefold-bonded atoms in a-Si exhibit a wide range of local environments-analogous to fivefold centers in coordination chemistry. Furthermore, it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for understanding defects in disordered materials beyond silicon alone. Silicon ; Atom (system on chip) ; Amorphous solid ; Amorphous silicon ; Materials science ; Nanotechnology ; Chemical physics ; Engineering physics ; Computer science ; Chemistry ; Crystallography ; Optoelectronics ; Engineering ; Crystalline silicon ; Parallel computing Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Yuming Jiang | Daniel DeBord | Heidi Vitrac | Jordan Stewart | Ali Haghani | Jennifer E. Van Eyk | Justyna Fert‐Bober | Jesse G. Meyer
Source: Journal of Proteome Research (review, 2024-05-07)
Field: Chemistry — Spectroscopy

The Future of Proteomics is Up in the Air: Can Ion Mobility Replace Liquid Chromatography for High Throughput Proteomics? The coevolution of liquid chromatography (LC) with mass spectrometry (MS) has shaped contemporary proteomics. LC hyphenated to MS now enables quantification of more than 10,000 proteins in a single injection, a number that likely represents most proteins in specific human cells or tissues. Separations by ion mobility spectrometry (IMS) have recently emerged to complement LC and further improve the depth of proteomics. Given the theoretical advantages in speed and robustness of IMS in comparison to LC, we envision that ongoing improvements to IMS paired with MS may eventually make LC obsolete, especially when combined with targeted or simplified analyses, such as rapid clinical proteomics analysis of defined biomarker panels. In this perspective, we describe the need for faster analysis that might drive this transition, the current state of direct infusion proteomics, and discuss some technical challenges that must be overcome to fully complete the transition to entirely gas phase proteomics. Proteomics ; Ion-mobility spectrometry ; Mass spectrometry ; Chemistry ; Chromatography ; Biomarker discovery ; Quantitative proteomics ; Computational biology ; Proteome ; Posttranslational modification ; Computer science ; Bioinformatics ; Biology ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Agnieszka Kozioł | Dominika Paliwoda | Grzegorz Mikiciuk | Nadhira Benhadji
Source: Agriculture (article, 2024-11-28)
Field: Materials Science — Biomaterials

Biochar as a Multi-Action Substance Used to Improve Soil Properties in Horticultural and Agricultural Crops—A Review With climate change escalating to global proportions over the past decade, along with a growing population, methods are being sought to help the natural and cultivated environment function in an ecologically balanced manner. Over the past few years, there has been a significantly increased interest in research on the use of natural substances for sustainable agriculture and horticulture. One of the most effective solutions to the emerging need is biochar, which has been the subject of environmental research for years due to its potential to increase soil carbon sequestration, reduce greenhouse gas emissions, remediate contaminated soil, and alleviate anthropogenic pressures. There is evidence of improved soil fertility and increased crop yields in agricultural production after biochar application. Our work comprehensively describes the effects of biochar on soil properties, crop productivity, and mitigating environmental stresses, and its remediation potential in heavy metal-contaminated soils. We analyzed a wide range of the literature on the most important properties of biochar for various potential uses. We summarized the results of research work over the past two decades to analyze soil and plant responses to biochar application. Biochar ; Agriculture ; Environmental science ; Agroforestry ; Agronomy ; Biology ; Waste management ; Ecology ; Engineering ; Pyrolysis Clay minerals and soil interactions ; Soil Carbon and Nitrogen Dynamics ; Agricultural Science and Fertilization
Authors: Chunguang Bi | Shuo Zhang | He Chen | Xinhua Bi | Jinjing Liu | Hao Xie | Helong Yu | Shaozhong Song | Lei Shi
Source: Agronomy (article, 2024-03-22)
Field: Chemistry — Analytical Chemistry

Non-Destructive Classification of Maize Seeds Based on RGB and Hyperspectral Data with Improved Grey Wolf Optimization Algorithms Ensuring the security of germplasm resources is of great significance for the sustainable development of agriculture and ecological balance. By combining the morphological characteristics of maize seeds with hyperspectral data, maize variety classification has been achieved using machine learning algorithms. Initially, the morphological data of seeds are obtained from images, followed by the selection of feature subsets using Recursive Feature Elimination (RFE) and Select From Model (SFM) methods, indicating that features selected by RFE exhibit better performance in maize seed classification. For hyperspectral data (350–2500 nm), Competitive Adaptive Re-weighted Sampling (CARS) and the Successive Projections Algorithm (SPA) are employed to extract feature wavelengths, with the SPA algorithm demonstrating superior performance in maize seed classification tasks. Subsequently, the two sets of data are merged, and a Random Forest (RF) classifier optimized by Grey Wolf Optimization (GWO) is utilized. Given the limitations of GWO, strategies such as logistic chaotic mapping for population initialization, random perturbation, and final replacement mechanisms are incorporated to enhance the algorithm’s search capabilities. The experimental results show that the proposed ZGWO-RF model achieves an accuracy of 95.9%, precision of 96.2%, and recall of 96.1% on the test set, outperforming the unimproved model. The constructed model exhibits improved identification effects on multi-source data, providing a new tool for non-destructive testing and the accurate classification of seeds in the future. Hyperspectral imaging ; Artificial intelligence ; RGB color model ; Pattern recognition (psychology) ; Computer science ; Algorithm ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Haozhi Sha | Yixuan Zhang | Yunpeng Ma | Wei Li | Wenfeng Yang | Jizhe Cui | Qian Li | Houbing Huang | Rong Yu
Source: Nature Communications (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

Polar vortex hidden in twisted bilayers of paraelectric SrTiO3 Polar topologies, such as vortex and skyrmion, have attracted significant interest due to their unique physical properties and promising applications in high-density memory devices. To date, all known polar vortices are present in or induced by ferroelectric materials. In this study, we find polar vortex arrays in paraelectric SrTiO<sub>3</sub>. Using multislice electron ptychography, the evolution of vorticity along the vortex axis is revealed in twisted bilayers of SrTiO<sub>3</sub> with deep-sub-angstrom resolution and one picometer accuracy. The surprising finding of polar vortices in a paraelectric crystal opens up opportunities for polarization physics and corresponding new devices. Polar ; Vortex ; Dielectric ; Condensed matter physics ; Chemical physics ; Polar vortex ; Materials science ; Nanotechnology ; Physics ; Optoelectronics ; Astronomy ; Meteorology Electronic and Structural Properties of Oxides ; Characterization and Applications of Magnetic Nanoparticles ; Atomic and Subatomic Physics Research
Authors: Aleksandr Käkinen | Yuhao Jiang | Thomas P. Davis | Tambet Teesalu | Märt Saarma
Source: International Journal of Nanomedicine (article, 2024-05-01)
Field: Materials Science — Biomaterials

Brain Targeting Nanomedicines: Pitfalls and Promise Brain diseases are the most devastating problem among the world's increasingly aging population, and the number of patients with neurological diseases is expected to increase in the future. Although methods for delivering drugs to the brain have advanced significantly, none of these approaches provide satisfactory results for the treatment of brain diseases. This remains a challenge due to the unique anatomy and physiology of the brain, including tight regulation and limited access of substances across the blood-brain barrier. Nanoparticles are considered an ideal drug delivery system to hard-to-reach organs such as the brain. The development of new drugs and new nanomaterial-based brain treatments has opened various opportunities for scientists to develop brain-specific delivery systems that could improve treatment outcomes for patients with brain disorders such as Alzheimer's disease, Parkinson's disease, stroke and brain tumors. In this review, we discuss noteworthy literature that examines recent developments in brain-targeted nanomedicines used in the treatment of neurological diseases. Brain disease ; Medicine ; Disease ; Drug delivery to the brain ; Brain cancer ; Blood–brain barrier ; Drug delivery ; Stroke (engine) ; Neuroscience ; Population ; Intensive care medicine ; Cancer ; Central nervous system ; Pathology ; Psychology ; Internal medicine ; Nanotechnology Nanoparticle-Based Drug Delivery ; Axon Guidance and Neuronal Signaling ; Barrier Structure and Function Studies
Authors: Zainab Rehman | Jairah Lubay | W. Trent Franks | Albert P. Bartók | Emily K. Corlett | Bao N. Nguyen | Garry Scrivens | Brian Samas | Heather Frericks-Schmidt | Steven P. Brown
Source: Faraday Discussions (article, 2024-05-20)
Field: Chemistry — Spectroscopy

Organic NMR crystallography: enabling progress for applications to pharmaceuticals and plant cell walls The application of NMR crystallography to organic molecules is exemplified by two case studies. For the tosylate salt of the active pharmaceutical ingredient, Ritlectinib, solid-state NMR spectra are presented at a <sup>1</sup>H Larmor frequency of 1 GHz and a magic-angle spinning (MAS) frequency of 60 kHz. Specifically, <sup>14</sup>N-<sup>1</sup>H heteronuclear multiple-quantum coherence (HMQC) and <sup>1</sup>H-<sup>1</sup>H double-quantum (DQ) single-quantum (SQ) correlation experiments are powerful probes of hydrogen bonding interactions. A full assignment of the <sup>1</sup>H, <sup>13</sup>C and <sup>14</sup>N/<sup>15</sup>N chemical shifts is achieved using also <sup>1</sup>H-<sup>13</sup>C cross polarization (CP) HETCOR spectra together with gauge-including projector augmented wave (GIPAW) DFT calculation for the geometry-optimised X-ray diffraction crystal structure that is reported here (CCDC 2352028). In addition, GIPAW calculations are presented for the <sup>13</sup>C chemical shifts in the two polymorphs of cellulose for which diffraction structures are available. For both case studies, a focus is on the discrepancy between experiment and GIPAW calculation. Chemistry ; Nanotechnology ; Computational chemistry ; Materials science Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Molecular spectroscopy and chirality
Authors: Qikai Fang | Zhihao Ye | Cheng Chen
Source: Electronics (review, 2024-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Review of Power Transformer Vibration and Noise Caused by Silicon Steel Magnetostriction As an important hub equipment in the power system, the power transformer has increasingly higher requirements for its reliability and safety with the rapid development of the power industry. However, the vibrations and noise generated during its operation not only cause a series of issues such as loose windings, core wear, and fatigue of fasteners, but also lead to anxiety and unease among nearby residents, severely affecting their physical and mental health. Therefore, the generation mechanism and control measures of vibration and noise in power transformers have attracted extensive attention from scholars. The magnetostrictive effect of silicon steel sheets is one of the important factors in the causes of transformer vibration and noise. This paper focuses on the magnetostrictive effect of silicon steel sheets. Firstly, the micro mechanism of magnetostriction is introduced. Secondly, based on current research status, the vibration and noise mechanism caused by magnetostriction are summarized, and the influencing factors, measurement, modeling, and noise reduction methods based on magnetostriction are summarized. Finally, based on the current research status and future development trends, prospects are proposed, aiming to provide references for subsequent research on transformer vibration and noise. Magnetostriction ; Transformer ; Vibration ; Electromagnetic coil ; Electrical steel ; Electrical engineering ; Engineering ; Noise (video) ; Electronic engineering ; Automotive engineering ; Materials science ; Acoustics ; Computer science ; Magnetic field ; Voltage ; Physics Magnetic Properties and Applications ; Power Transformer Diagnostics and Insulation ; Non-Destructive Testing Techniques