jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jessalyn G. Miller | Spencer A. Hughes | Charles Modlin | Vincent P. Conticello
Source: Quarterly Reviews of Biophysics (article, 2022-01-01)
Field: Materials Science — Biomaterials

Structures of synthetic helical filaments and tubes based on peptide and peptido-mimetic polymers Abstract Synthetic peptide and peptido-mimetic filaments and tubes represent a diverse class of nanomaterials with a broad range of potential applications, such as drug delivery, vaccine development, synthetic catalyst design, encapsulation, and energy transduction. The structures of these filaments comprise supramolecular polymers based on helical arrangements of subunits that can be derived from self-assembly of monomers based on diverse structural motifs. In recent years, structural analyses of these materials at near-atomic resolution (NAR) have yielded critical insights into the relationship between sequence, local conformation, and higher-order structure and morphology. This structural information offers the opportunity for development of new tools to facilitate the predictable and reproducible de novo design of synthetic helical filaments. However, these studies have also revealed several significant impediments to the latter process – most notably, the common occurrence of structural polymorphism due to the lability of helical symmetry in structural space. This article summarizes the current state of knowledge on the structures of designed peptide and peptido-mimetic filamentous assemblies, with a focus on structures that have been solved to NAR for which reliable atomic models are available. Peptide ; Supramolecular chemistry ; Polymer ; Monomer ; Fiber diffraction ; Structural biology ; Protein structure Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Chemical Synthesis and Analysis
Authors: Lian Li | Zhi‐Tian Zuo | Yuanzhong Wang
Source: Frontiers in Microbiology (article, 2022-07-08)
Field: Chemistry — Analytical Chemistry

Practical Qualitative Evaluation and Screening of Potential Biomarkers for Different Parts of Wolfiporia cocos Using Machine Learning and Network Pharmacology <i>Wolfiporia cocos</i> is a widely used traditional Chinese medicine and dietary supplement. Artificial intelligence algorithms use different types of data based on the different strategies to complete multiple tasks such as search and discrimination, which has become a trend to be suitable for solving massive data analysis problems faced in network pharmacology research. In this study, we attempted to screen the potential biomarkers in different parts of <i>W. cocos</i> from the perspective of measurability and effectiveness based on fingerprint, machine learning, and network pharmacology. Based on the conclusions drawn from the results, we noted the following: (1) exploratory analysis results showed that differences between different parts were greater than those between different regions, and the partial least squares discriminant analysis and residual network models were excellent to identify <i>Poria</i> and <i>Poriae</i> cutis based on Fourier transform near-infrared spectroscopy spectra; (2) from the perspective of effectiveness, the results of network pharmacology showed that 11 components such as dehydropachymic acid and 16α-hydroxydehydrotrametenolic acid, and so on had high connectivity in the "component-target-pathway" network and were the main active components. (3) From a measurability perspective, through orthogonal partial least squares discriminant analysis and the variable importance projection > 1, it was confirmed that three components, namely, dehydrotrametenolic acid, poricoic acid A, and pachymic acid, were the main potential biomarkers based on high-performance liquid chromatography. (4) The content of the three components in <i>Poria</i> was significantly higher than that in <i>Poriae</i> cutis. (5) The integrated analysis showed that dehydrotrametenolic acid, poricoic acid A, and pachymic acid were the potential biomarkers for <i>Poria</i> and <i>Poriae</i> cutis. Overall, this approach provided a novel strategy to explore potential biomarkers with an explanation for the clinical application and reasonable development and utilization in <i>Poria</i> and <i>Poriae</i> cutis. Computational biology ; Artificial intelligence ; Biology ; Machine learning ; Pharmacology ; Computer science ; Traditional medicine ; Medicine Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Identification and Quantification in Food
Authors: Wei He | Guanyu Ma | Quanli Shen | Zhenghua Tang
Source: Nanomaterials (review, 2022-05-19)
Field: Materials Science — Materials Chemistry

Engineering Gold Nanostructures for Cancer Treatment: Spherical Nanoparticles, Nanorods, and Atomically Precise Nanoclusters Cancer is a major global health issue and is a leading cause of mortality. It has been documented that various conventional treatments can be enhanced by incorporation with nanomaterials. Thanks to their rich optical properties, excellent biocompatibility, and tunable chemical reactivities, gold nanostructures have been gaining more and more research attention for cancer treatment in recent decades. In this review, we first summarize the recent progress in employing three typical gold nanostructures, namely spherical Au nanoparticles, Au nanorods, and atomically precise Au nanoclusters, for cancer diagnostics and therapeutics. Following that, the challenges and the future perspectives of this field are discussed. Finally, a brief conclusion is summarized at the end. Nanoclusters ; Nanorod ; Nanotechnology ; Nanomaterials ; Materials science ; Nanostructure ; Nanoparticle ; Biocompatibility ; Colloidal gold ; Metallurgy Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Sebastian J. Wetzel | Kevin Ryczko | Roger G. Melko | Isaac Tamblyn
Source: Applied AI Letters (article, 2022-10-04)
Field: Materials Science — Materials Chemistry

Twin neural network regression Abstract We introduce twin neural network regression (TNNR). This method predicts differences between the target values of two different data points rather than the targets themselves. The solution of a traditional regression problem is then obtained by averaging over an ensemble of all predicted differences between the targets of an unseen data point and all training data points. Whereas ensembles are normally costly to produce, TNNR intrinsically creates an ensemble of predictions of twice the size of the training set while only training a single neural network. Since ensembles have been shown to be more accurate than single models this property naturally transfers to TNNR. We show that TNNs are able to compete or yield more accurate predictions for different data sets, compared with other state‐of‐the‐art methods. Furthermore, TNNR is constrained by self‐consistency conditions. We find that the violation of these conditions provides a signal for the prediction uncertainty. Regression ; Consistency (knowledge bases) ; Artificial neural network ; Regression analysis ; Computer science ; Property (philosophy) ; Artificial intelligence ; Set (abstract data type) ; Linear regression ; Training set ; Data set ; Machine learning ; Statistics ; Pattern recognition (psychology) ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications ; Fault Detection and Control Systems
Authors: Eibar Flores | Nataliia Mozhzhukhina | Xinyu Li | Poul Norby | Aleksandar Matic | Tejs Vegge
Source: Chemistry - Methods (article, 2022-02-19)
Field: Chemistry — Electrochemistry

PRISMA: A Robust and Intuitive Tool for High‐Throughput Processing of Chemical Spectra** Abstract Here we introduce PRISMA, an open‐source tool to rapidly analyse large numbers of spectra into meaningful spectroscopic trends. PRISMA follows a human‐in‐the‐loop workflow, where the user interacts with an intuitive graphical user interface to control multiple steps in the spectrum analysis process: trimming, baseline correction, and peak fitting. The tool outputs the results in an easy‐to‐read csv format within seconds. We describe the functionalities implemented in PRISMA and test its capabilities with three experimental cases relevant to the study of Electrochemical energy storage and conversion devices: a temperature‐dependent Raman measurement of phase transitions, a linear Raman mapping of a graphite composite electrode, and an operando X‐ray diffraction experiment of lithium nickel oxide composite battery electrode. The case studies demonstrate the robustness of the app and its ability to unearth insightful trends in peak parameters, which are easier to represent, interpret and further analyse with more advanced techniques. Throughput ; Computer science ; Telecommunications Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Spectroscopy and Chemometric Analyses
Authors: Siya Nakapraves | Monika Warzecha | Chantal L. Mustoe | Vijay K. Srirambhatla | Alastair J. Florence
Source: Pharmaceutical Research (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Prediction of Mefenamic Acid Crystal Shape by Random Forest Classification Random forest classification models using solvent physical property descriptors can reliably predict crystal morphologies for mefenamic acid crystals grown in 20 out of the 28 solvents included in this work. Poor prediction accuracies for the remaining 8 solvents indicate that further factors will be required in the feature set to provide a more generalized predictive morphology model. Mefenamic acid ; Random forest ; Crystal (programming language) ; Solvent ; Molecular descriptor ; Cross-validation ; Biological system ; Artificial intelligence ; Particle size ; Predictive modelling ; Feature (linguistics) ; Materials science ; Machine learning ; Chemistry ; Quantitative structure–activity relationship ; Computer science ; Chromatography ; Organic chemistry ; Physical chemistry Crystallization and Solubility Studies ; Cultural Heritage Materials Analysis ; Analytical Chemistry and Chromatography
Authors: Gaetano Calogero | Domenica Raciti | Pablo Acosta-Alba | F. Cristiano | Ioannis Deretzis | Giuseppe Fisicaro | Karim Huet | S. Kerdilès | Alberto Sciuto | Antonino La Magna
Source: npj Computational Materials (article, 2022-03-11)
Field: Materials Science — Materials Chemistry

Multiscale modeling of ultrafast melting phenomena Abstract Ultraviolet Nanosecond Laser Annealing (LA) is a powerful tool for both fundamental investigations of ultrafast, nonequilibrium phase-change phenomena and technological applications (e.g., the processing of 3D sequentially integrated nano-electronic devices) where strongly confined heating and melting is desirable. Optimizing the LA process along with the experimental design is challenging, especially when involving complex 3D-nanostructured systems with various shapes and phases. To this purpose, it is essential to model critical nanoscale physical LA-induced phenomena, such as shape changes or formation and evolution of point and extended defects. To date, LA simulators are based on continuum models, which cannot fully capture the microscopic kinetics of a solid–liquid interface. In this work a fully atomistic LA simulation methodology is presented, based on the parallel coupling of a continuum, finite elements, μm-scale electromagnetic-thermal solver with a super-lattice Kinetic Monte Carlo atomistic model for melting. Benchmarks against phase-field models and experimental data validate the approach. LA of a Si(001) surface is studied varying laser fluence and pulse shape, assuming both homogeneous and inhomogeneous nucleation, revealing how liquid Si nuclei generate, deform and coalesce during irradiation. The proposed methodology is applicable to any system where the atom kinetics is determined by a strongly space- and time-dependent field, such as temperature or strain. Materials science ; Non-equilibrium thermodynamics ; Nucleation ; Kinetic Monte Carlo ; Thermal ; Monte Carlo method ; Statistical physics ; Chemical physics ; Physics ; Thermodynamics Solidification and crystal growth phenomena ; nanoparticles nucleation surface interactions ; Aluminum Alloy Microstructure Properties
Authors: Hoda Galal | Salah Elsayed | Osama Elsherbiny | Aida Allam | Mohamed Farouk
Source: Agriculture (article, 2022-09-27)
Field: Chemistry — Analytical Chemistry

Using RGB Imaging, Optimized Three-Band Spectral Indices, and a Decision Tree Model to Assess Orange Fruit Quality Point samples and laboratory testing have historically been used to evaluate fruit quality criteria. Although this method is precise, it is slow, expensive, and destructive, making it unsuitable for large-scale monitoring of these parameters. The main objective of this research was to develop a non-invasive protocol by combining color RGB indices (CIs) and previously published and newly developed three-band spectral reflectance indices (SRIs) with a decision tree (DT) model to evaluate the fruit quality parameters of navel orange. These parameters were brightness (L*), red–green (a*), blue–yellow (b*), chlorophyll meter (Chlm), total soluble solids (TSS), and TSS/acid ratio. The characteristics of fruit quality of navel orange samples were measured at various stages of ripening. The outcomes demonstrated that at various levels of ripening, the fruit quality parameters, RGB imaging indices, and published and newly developed three-band SRIs differed. The newly developed three-band SRIs based on the wavelengths of blue, green, red, red-edge, and NIR are most effective for estimating the six measured parameters in this study. For example, NDI574,592,724, NDI572,584,724, and NDI574,722,590 had the largest R2 value (0.90) with L*, whereas NDI526,664,700 and NDI524,700,664 exhibited the highest R2 value (0.97) with a*. Moreover, integrating CIs and SRIs with the DT model has provided a potentially useful tool for the accurate measurement of the six studied parameters. For instance, the DT-SRIs-CIs-30 model performed better in terms of measuring a* using 30 various indices. The R2 value was 0.98 and RMSE = 1.121 in the cross-validation, while R2 value was 0.964 and RMSE = 2.604 in the test set. Otherwise, based on the fusion of five various indices, the DT-SRIs-CIs-5 model was the most precise for recognizing b* (R2 = 0.957 and 0.929, with RMSE = 1.713 and 3.309 for cross-validation and test set, respectively). Overall, this work proves that integrating the different characteristics of proximal reflectance sensing systems such as color RGB indices and SRIs via the DT model may be considered a reliable instrument for evaluating the quality of different fruits. RGB color model ; Orange (colour) ; Hue ; Navel orange ; Reflectivity ; Mathematics ; Ripening ; Brightness ; Environmental science ; Remote sensing ; Horticulture ; Computer science ; Artificial intelligence ; Biology ; Optics ; Physics ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Mafalda Reis Pereira | Renan Tosin | Rui C. Martins | Filipe Neves dos Santos | Fernando Tavares | Mário Cunha
Source: Plants (article, 2022-08-19)
Field: Chemistry — Analytical Chemistry

Kiwi Plant Canker Diagnosis Using Hyperspectral Signal Processing and Machine Learning: Detecting Symptoms Caused by Pseudomonas syringae pv. actinidiae <i>Pseudomonas syringae</i> pv. <i>actinidiae</i> (Psa) has been responsible for numerous epidemics of bacterial canker of kiwi (BCK), resulting in high losses in kiwi production worldwide. Current diagnostic approaches for this disease usually depend on visible signs of the infection (disease symptoms) to be present. Since these symptoms frequently manifest themselves in the middle to late stages of the infection process, the effectiveness of phytosanitary measures can be compromised. Hyperspectral spectroscopy has the potential to be an effective, non-invasive, rapid, cost-effective, high-throughput approach for improving BCK diagnostics. This study aimed to investigate the potential of hyperspectral UV-VIS reflectance for in-situ, non-destructive discrimination of bacterial canker on kiwi leaves. Spectral reflectance (325-1075 nm) of twenty plants were obtained with a handheld spectroradiometer in two commercial kiwi orchards located in Portugal, for 15 weeks, totaling 504 spectral measurements. Several modeling approaches based on continuous hyperspectral data or specific wavelengths, chosen by different feature selection algorithms, were tested to discriminate BCK on leaves. Spectral separability of asymptomatic and symptomatic leaves was observed in all multi-variate and machine learning models, including the FDA, GLM, PLS, and SVM methods. The combination of a stepwise forward variable selection approach using a support vector machine algorithm with a radial kernel and class weights was selected as the final model. Its overall accuracy was 85%, with a 0.70 kappa score and 0.84 F-measure. These results were coherent with leaves classified as asymptomatic or symptomatic by visual inspection. Overall, the findings herein reported support the implementation of spectral point measurements acquired in situ for crop disease diagnosis. Pseudomonas syringae ; Hyperspectral imaging ; Kiwi ; Biology ; Canker ; Botany ; Artificial intelligence ; Horticulture ; Computer science ; Microbiology ; Pathogen Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Xiaoge Zhang | Fuyao Liu | Zhen Gu
Source: BME Frontiers (review, 2022-12-28)
Field: Materials Science — Biomaterials

Tissue Engineering in Neuroscience: Applications and Perspectives Neurological disorders have always been a threat to human physical and mental health nowadays, which are closely related to the nonregeneration of neurons in the nervous system (NS). The damage to the NS is currently difficult to repair using conventional therapies, such as surgery and medication. Therefore, repairing the damaged NS has always been a vast challenge in the area of neurology. Tissue engineering (TE), which integrates the cell biology and materials science to reconstruct or repair organs and tissues, has widespread applications in bone, periodontal tissue defects, skin repairs, and corneal transplantation. Recently, tremendous advances have been made in TE regarding neuroscience. In this review, we summarize TE's recent progress in neuroscience, including pathological mechanisms of various neurological disorders, the concepts and classification of TE, and the most recent development of TE in neuroscience. Lastly, we prospect the future directions and unresolved problems of TE in neuroscience. Neuroscience ; Cognitive science ; Psychology Electrospun Nanofibers in Biomedical Applications ; 3D Printing in Biomedical Research ; Tissue Engineering and Regenerative Medicine
Authors: Константин В. Саватеев | Victor V. Fedotov | В. Л. Русинов | S. K. Kotovskaya | А. А. Спасов | A. F. Kucheryavenko | Pavel M. Vasiliev | В. А. Косолапов | В. С. Сиротенко | К. А. Гайдукова | Georgiy M. Uskov
Source: Molecules (article, 2022-01-02)
Field: Chemistry — Organic Chemistry

Azolo[1,5-a]pyrimidines and Their Condensed Analogs with Anticoagulant Activity Hypercytokinemia, or cytokine storm, is one of the severe complications of viral and bacterial infections, involving the release of abnormal amounts of cytokines, resulting in a massive inflammatory response. Cytokine storm is associated with COVID-19 and sepsis high mortality rate by developing epithelial dysfunction and coagulopathy, leading to thromboembolism and multiple organ dysfunction syndrome. Anticoagulant therapy is an important tactic to prevent thrombosis in sepsis and COVID-19, but recent data show the incompatibility of modern direct oral anticoagulants and antiviral agents. It seems relevant to develop dual-action drugs with antiviral and anticoagulant properties. At the same time, it was shown that azolo[1,5-<i>a</i>]pyrimidines are heterocycles with a broad spectrum of antiviral activity. We have synthesized a new family of azolo[1,5-<i>a</i>]pyrimidines and their condensed polycyclic analogs by cyclocondensation reactions and direct CH-functionalization and studied their anticoagulant properties. Five compounds among 1,2,4-triazolo[1,5-<i>a</i>]pyrimidin-7-ones and 5-alkyl-1,3,4-thiadiazolo[3,2-<i>a</i>]purin-8-ones demonstrated higher anticoagulant activity than the reference drug, dabigatran etexilate. Antithrombin activity of most active compounds was confirmed using lipopolysaccharide (LPS)-treated blood to mimic the conditions of cytokine release syndrome. The studied compounds affected only the thrombin time value, reliably increasing it 6.5-15.2 times as compared to LPS-treated blood. Cytokine storm ; Sepsis ; Antithrombin ; Anticoagulant ; Cytokine ; Pharmacology ; Lipopolysaccharide ; Coagulopathy ; Medicine ; Drug ; Chemistry ; Immunology ; Internal medicine ; Coronavirus disease 2019 (COVID-19) ; Heparin Quinazolinone synthesis and applications ; Synthesis and biological activity ; Synthesis and Biological Evaluation
Authors: Adilet N. Toksumakov | Georgy A. Ermolaev | Aleksandr S. Slavich | Natalia V. Doroshina | Ekaterina V. Sukhanova | Dmitry I. Yakubovsky | Alexander V. Syuy | Sergey M. Novikov | Р. И. Романов | Andrey M. Markeev | Aleksandr S. Oreshonkov | Dmitry Tsymbarenko | Захар И. Попов | Dmitry G. Kvashnin | Andrey A. Vyshnevyy | Aleksey V. Arsenin | Davit Ghazaryan | Valentyn S. Volkov
Source: npj 2D Materials and Applications (article, 2022-11-24)
Field: Materials Science — Materials Chemistry

High-refractive index and mechanically cleavable non-van der Waals InGaS3 Abstract The growing family of two-dimensional crystals has been recognized as a promising platform for investigation of rich low-dimension physics and production of a variety of devices. Of particular interest are recently reported atomic sheets of non-van der Waals materials, which reshape our understanding of chemical bonds and enable heterostructures with novel functionality. Here, we study the structural and optical properties of ultrathin non-van der Waals InGaS 3 sheets produced by standard mechanical cleavage. Our ab initio calculations reveal weak out-of-plane covalent bonds, responsible for the layered structure of the material. The energy required for isolation of a single layer is as low as ~50 meVÅ –2 , which is comparable with the conventional van der Waals material’s monolayer isolation energies of 20–60 meVÅ –2 . A comprehensive study of the structural, vibrational, and optical properties of the material reveals its wide bandgap (2.73 eV), high refractive index (&gt;2.5) and negligible losses in the visible and infrared spectral ranges. These properties make it a perfect candidate for visible-range all-dielectric nanophotonics. van der Waals force ; Refractive index ; Monolayer ; Materials science ; Band gap ; Dielectric ; Covalent bond ; Ab initio ; Non-covalent interactions ; Van der Waals strain ; Nanophotonics ; Ab initio quantum chemistry methods ; Chemical physics ; Van der Waals radius ; Nanotechnology ; Molecular physics ; Chemistry ; Optoelectronics ; Molecule ; Hydrogen bond ; Organic chemistry 2D Materials and Applications ; MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research