jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kafa Khalaf
Source: Iraqi Journal of Industrial Research (article, 2022-10-20)
Field: Chemistry — Spectroscopy

In Silico Taste–Toxicity Study of New Hypothetical Hetero-Abiraterone Derivatives First Iraqi attempt to study drug used for prostate cancer treatment (Abiraterone) that hypothetically reacted with known chemicals classified as Anticancer drug: Tirapazamine (T) and 5-Fluorouracil (F), food additive and preservative: Butylated Hydroxy Toluene (B) and Ethoxyquin (E), and sweeteners: P-4000 (P), Sodium Cyclamate (CS), Alitame (AT), and Saccharin (SA). The second step in this work was computational study of all reactants and the formed products having newly ether, amine, and carboxylic acid ester, and sulphonate bonds by online websites. Taste, toxicity, and ADMET were calculated by three online websites related to Charite University of Medicine, Institute for Physiology, Germany and University of Melbourne, Australia. SMILES of the reactants were obtained from National Library of Medicine/ National Center for Biotechnology Information websites while products were drawn by the molecular editor CS ChemDraw Ultra and rechecked by MarvinSketch program. This in Silico study showed various results of the formed products compared to Abiraterone (A) that predicated it as sour chemical belongs to Class 4 as a harmful substance if swallowed. Abiraterone (A) toxicity on liver organ was 61% probability percentage as hepatotoxicity while carcinogenicity, Immunotoxicity, Mutagenicity, cytotoxicity, AhR, AR, Aromatase, ER, HSE, and p53 were more than 70 % to bind Progesterone or Androgen. Also, Abiraterone (A) has a poor water solubility leading to high intestinal absorption, moderate total clearance, and giving inhibition reaction to Cytochrome P450 type CYP2C19, hERG II, and Ames test. These results confirmed that Abiraterone is structurally less harmful acute class with highly chance to interact with cell components resulting lethal response. All Abiraterone hypothetical products had a harmful reaction if swelled (Class 4), sour taste. All toxicological characters may be highly affected by its water solubility and intestinal absorption towards CNS, BBB, and CaCO2 permeability, skin sensation, and Ames test issues. For example, this in Silico- QSAR foundations about Abiraterone – Saccharin (A-SA) suggest that A-SA is structurally safe and there are several possibilities of becoming an active–multiple toxicological compound. Chemistry ; Pharmacology ; Ames test ; Toxicity ; Acute toxicity ; Toxicology ; Medicine ; Organic chemistry ; Biology Analytical Chemistry and Chromatography ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Sergey P. Verevkin | Artemiy Samarov
Source: Research Square (preprint, 2022-10-10)
Field: Chemistry — Organic Chemistry

Thermochemistry in 21 st century – Quo Vadis? In silico assisted diagnostics of available thermochemical data Abstract Which comes first, experiment or theory? The answer is obvious – the experiment comes first. But how to be sure that the result of the experiment is reliable? Perhaps the crucial criterion is that the result should be consistent with the network of knowledge already available. In this study, we propose a step-by-step algorithm for quality diagnostics of thermochemical data on enthalpies of formation and enthalpies of phase transitions of organic compounds. The consistency of the data is studied and established using empirical structure-property correlations as well as using high-level quantum chemical calculations. The diagnostic algorithm is exemplarily demonstrated on a series of alkyl-substituted benzophenones for which conflicting thermochemical data were available. Thermochemistry ; Quantum chemical ; Consistency (knowledge bases) ; Experimental data ; Standard enthalpy of formation ; Property (philosophy) ; Status quo ; Computer science ; Chemistry ; Computational chemistry ; Mathematics ; Physical chemistry ; Molecule ; Organic chemistry ; Artificial intelligence ; Statistics ; Economics ; Epistemology Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Tyler D. R. Vance | Patrick Yip | Elisabet Jiménez | Sheng Li | Diana Gawol | James Byrnes | Isabel Usón | Ahmed Ziyyat | Jeffrey E. Lee
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-03-23)
Field: Materials Science — Materials Chemistry

SPACA6 structure reveals a conserved superfamily of gamete fusion-associated proteins Abstract SPACA6 is a sperm-expressed surface protein that is critical for gamete fusion during mammalian sexual reproduction. Despite this fundamental role, little is known about how SPACA6 specifically functions. We elucidated the crystal structure of SPACA6 at 2.2-Å resolution, revealing a two-domain protein containing a four-helix bundle and Ig-like β-sandwich connected via a quasi-flexible linker. Based on the structural analysis, we propose SPACA6 is a founding member of a superfamily of gamete fusion-associated proteins, herein dubbed the IST superfamily. The IST superfamily is defined structurally by its distorted four-helix bundle and a pair of disulfide-bonded CXXC motifs. A structure-based search of the AlphaFold human proteome identified more protein members to this superfamily; remarkably, many of these proteins are linked to gamete fusion. The SPACA6 structure and its connection to other IST-superfamily members provide a missing link in our knowledge of mammalian gamete fusion. Significance Statement SPACA6 is a human sperm protein vital for the fusion of gametes, though its exact function remains a mystery. We present the first solved structure of SPACA6: a two-domain fold comprised of an Ig-like domain and a distorted four-helix bundle. Dali searches of the PDB and AlphaFold reveal a family of structurally related proteins, several of which are also known to play a role in gamete fusion; as such, SPACA6 is a founding member of a conserved protein superfamily, dubbed the IST superfamily. Evolutionary analysis to ascertain functionally relevant structural elements in SPACA6 show a conservation of flexibility between the two domains and several conserved surfaces that could function as protein-protein interfaces. Gamete ; Fusion protein ; Biology ; SUPERFAMILY ; Immunoglobulin superfamily ; Protein Data Bank (RCSB PDB) ; Protein domain ; Conserved sequence ; Protein structure ; Proteome ; Protein family ; Genetics ; Cell biology ; Computational biology ; Peptide sequence ; Sperm ; Biochemistry ; Receptor ; Gene ; Recombinant DNA Enzyme Structure and Function ; Protein Structure and Dynamics ; Biochemical and Structural Characterization
Authors: Emmanuel Ahishakiye | Waweru Mwangi | Petronilla Muthoni | Fredrick Kanobe | Godliver Owomugisha | Danison Taremwa | Leonard Nkalubo
Source: Research Square (preprint, 2022-06-16)
Field: Chemistry — Analytical Chemistry

Deep Gaussian Convolutional Neural Network Model in Classification of Cassava Diseases using Spectral Data Abstract Early disease identification in crops is critical for food security, especially in Sub-Saharan Africa. To identify cassava diseases, professionals visually score the plants by looking for disease indicators on the leaves which is notoriously subjective. Automating the detection and classification of crop diseases could help professionals diagnose diseases more accurately and allow farmers in remote locations to monitor their crops without the help of specialists. Machine learning algorithms have been used in the early detection and classification of crop diseases. However, traditional machine learning algorithms are not calibrated even though they have high accuracy. The ability to provide well-calibrated posterior distributions is one of the most appealing properties of Gaussian processes. Motivated by the current developments in the field of Gaussian Processes, this study proposed a deep Gaussian convolutional neural network model (DGCNN) for the detection and classification of cassava diseases using spectral data. The proposed model uses a hybrid kernel function that is the product of a rational quadratic kernel and a squared exponential kernel. Experimental results revealed that our proposed hybrid kernel function performed better in terms of accuracy of 90.10% when compared to both the squared exponential kernel with an accuracy of 88.01% and the rational quadratic kernel with an accuracy of 88.52%. In our future work, we propose to integrate the Optimised model proposed in this study with the transfer learning approach, a move that may help to improve the model performance. Gaussian function ; Kernel (algebra) ; Computer science ; Machine learning ; Artificial intelligence ; Gaussian ; Convolutional neural network ; Pattern recognition (psychology) ; Quadratic equation ; Gaussian network model ; Gaussian process ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Mohammad Arabi | Mohammad Dahmardeh Ghalehno
Source: Research Square (preprint, 2022-07-27)
Field: Materials Science — Polymers and Plastics

A sutible model for drying kenitic of wood fiber using halogen moistuer analayzer Fiber ; Materials science ; Moisture ; Water content ; Empirical modelling ; Diffusion ; Composite material ; Coefficient of determination ; Mass transfer ; Thermodynamics ; Chemistry ; Mathematics ; Chromatography ; Statistics ; Simulation ; Physics Textile materials and evaluations ; Natural Fiber Reinforced Composites ; Wood Treatment and Properties
Authors: Md. Iqbal Quraishi
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2022-08-31)
Field: Chemistry — Analytical Chemistry

A Model for Classifying Rice Varieties Grown in Turkey Using Image-Based Morphological Features and Machine Learning Abstract: Rice is one of the important cereals which feeds more than half of the world's population. It is used frequently in a variety of flavourful recipes. In this work, a dataset of Cammeo and Osmancik species found in Turkey has been selected for the study. It have 3810 samples containing seven morphological features. The feature ranking methods like Fisher score, FSV, infFS, Laplacian, ReliefF, MCFS and MUTinfFS are selected and applied on the above datasets for the purpose of finding key features for proper feature selection. After selecting key feature values, feature vector have been prepared. Then, Support vector machine technique was applied for classification based on the results obtained from feature ranking techniques. Feature selection ; Ranking (information retrieval) ; Pattern recognition (psychology) ; Feature (linguistics) ; Artificial intelligence ; Support vector machine ; Key (lock) ; Computer science ; Selection (genetic algorithm) ; Feature vector ; Variety (cybernetics) ; Population ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Identification and Quantification in Food
Authors: Taís Téo de Barros-Alexandrino1 | Milena Tosi Martelli | O. B. G. Assis
Source: Research Square (preprint, 2022-12-12)
Field: Materials Science — Biomaterials

In vitro and in vivo antifungal activity of chitosan, as continuous gel and nanoparticles suspension, against Alternaria alternata and Penicillium expansum. Alternaria alternata ; Penicillium expansum ; In vitro ; In vivo ; Antifungal ; Chitosan ; Microbiology ; Chemistry ; Fungal pathogen ; Suspension (topology) ; Fungi imperfecti ; Postharvest ; Biology ; Horticulture ; Pathogen ; Biotechnology ; Biochemistry Nanocomposite Films for Food Packaging ; Essential Oils and Antimicrobial Activity ; Antimicrobial agents and applications
Authors: İrem KILINÇ | Berna Kılınç
Source: Journal of Limnology and Freshwater Fisheries Research (article, 2022-12-21)
Field: Materials Science — Biomaterials

Recent Advances in Packaging Technology of Seafood Products Packaging is developing about the development of consumer demands and awareness day by day. Packaging technology is a technology that is constantly evolving. Monitoring seafood products at all stages from the moment they are caught to the moment they are consumed is extremely important for the quality of seafood products and prevention of diseases caused by seafood. Therefore, by using of improved packaging techniques, not only can be prevented the contamination of fishery products with microorganisms, but also the quality and suitability of fishery products for consumption can also be monitored at every stage. In this review, conducted studies regarding advanced packaging materials and products for seafood products have been mentioned and studies on recent advanced packaging applications in fresh and processed fishery products in recent years were compiled. Business ; Quality (philosophy) ; Food packaging ; Active packaging ; Environmental science ; Packaging engineering ; Food science ; Marketing ; Biology Nanocomposite Films for Food Packaging ; Meat and Animal Product Quality ; Protein Hydrolysis and Bioactive Peptides
Authors: Iylia Adhwa Mazni | Samsul Setumin | Mohamed Syazwan Osman | Khusairi Osman | Mohd Subri Tahir
Source: Journal of Electrical & Electronic Systems Research (article, 2022-11-01)
Field: Chemistry — Analytical Chemistry

Systematic Literature Review Approach on the Fruit Quality Assessment Based on Fruit Imaging Techniques Non-destructive quality assessment is one of the methods in image processing used to evaluate the qualities of fruits without destroying the internal structure and external appearance. Imaging processing techniques and machine learning methods have emerged as an effective way to evaluate fruit quality assessment, which helps to classify the fruit's quality, especially during the harvest process. Image processing significantly shifts in fields, especially agriculture, medical, marketing profiling and vehicles. The rapid progress in agriculture continually increases the demand for up-to-date and accurate data to characterise the modality of imaging techniques used to evaluate the quality of fruits, which helps to guide and support research decisions, especially for future researchers that are new. This systematic literature review will focus on the type of fruits, type of modalities, pre-processing of imaging techniques and classification experiments that had been studied in recent years using planning, conducting and reporting methods. Through this study, 486 papers were selected at a preliminary stage and narrowed down to 35 papers that had been investigated from specialised research that has indicated the preferential types of data and regions of interest in image processing. In addition, imaging technique papers published between 2016 to 2022 have been studied, discussed and analysed. The presented finding outlines important research determination whose regardful report is of great value to this research community. Quality assessment ; Quality (philosophy) ; Engineering ; Evaluation methods ; Reliability engineering ; Epistemology ; Philosophy Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Hongxin Wang | Yoshitaka Yoda | Jessie Wang
Source: Physchem (article, 2022-11-28)
Field: Chemistry — Spectroscopy

The True Nature of the Energy Calibration for Nuclear Resonant Vibrational Spectroscopy: A Time-Based Conversion Nuclear resonant vibrational spectroscopy (NRVS) is an excellent synchrotron-based vibrational spectroscopy. Its isotope specificity and other advantages are particularly good to study, for example, iron center(s) inside complicated molecules such as enzymes. In order to investigate some small energy shifts, the energy scale variation from scan to scan must be corrected via an in-situ measurement or with other internal reference peak(s) inside the spectra to be calibrated. On the other hand, the energy re-distribution within each scan also needs attention for a sectional scan which has a different scanning time per point in different sections and is often used to measure weak NRVS signals. In this publication, we: (1) evaluated the point-to-point energy re-distribution within each NRVS scan or within an averaged scan with a time-scaled (not energy-scaled) function; (2) discussed the errorbar contributed from the improper “distribution” of ΔEi or the averaged ΔE within one scan (Eerr1) vs. that due to the different ΔEi from different scans (Eerr2). It is well illustrated that the former (Eerr1) is as important as, or sometimes even more important than, the latter (Eerr2); and (3) provided a procedure to re-calibrate the published NRVS-derived PVDOS spectra in case of need. This article establishes the concept that, at least for sectional NRVS scans, the energy positions should be corrected according to the time scanned rather than be scaled with a universal constant, as in a conventional calibration procedure. Calibration ; Synchrotron ; Energy (signal processing) ; Spectroscopy ; Measure (data warehouse) ; Spectral line ; Distribution (mathematics) ; Physics ; Chemistry ; Materials science ; Computational physics ; Optics ; Mathematics ; Computer science ; Mathematical analysis Advanced NMR Techniques and Applications ; Crystallography and Radiation Phenomena ; Radioactive element chemistry and processing
Authors: Kan Hatakeyama‐Sato | Yasuhiko Igarashi | Takahiro Kashikawa | Koichi Kimura | Kenichi Oyaizu
Source: ChemRxiv (preprint, 2022-08-24)
Field: Materials Science — Materials Chemistry

Quantum circuit learning as a potential algorithm to predict experimental chemical properties We introduce quantum circuit learning (QCL) as an emerging regression algorithm for chemo- and materials-informatics. The supervised model, functioning on the rule of quantum mechanics, can process linear and smooth non-linear functions from small datasets (< 100 records). Compared with conventional algorithms, such as random forest, support vector machine, and linear regressions, the QCL can offer better predictions with some one-dimensional functions and experimental chemical databases. QCL will potentially help the virtual exploration of new molecules and materials more efficiently through its superior prediction performances. Support vector machine ; Random forest ; Quantum chemical ; Computer science ; Process (computing) ; Algorithm ; Quantum ; Machine learning ; Molecule ; Chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ayfer Bahar | Volkan Aydın | Caner Vızdıklar | Ahmet Akıcı
Source:  (preprint, 2022-02-11)
Field: Chemistry — Spectroscopy

The association of chiral characteristic with drug withdrawal due to safety: a comparative analysis Aim: Chirality of drugs might be associated with safety issues through pharmacokinetic or pharmacodynamic variations, interactions, or direct toxicological responses. This study aimed to examine chiral status of the drugs withdrawn from the market. Methods: We searched the literature regarding withdrawn drugs between 1950-2020 due to safety-related issues and identified 395 drugs. We examined their chirality and assigned into one of three categories: achiral compound, chiral mixture, and pure enantiomer. We compared their distribution at ATC-1 level, duration on the market, and adverse drug reactions leading to their withdrawal. Results: We identified that 52.4% (n=207) of withdrawn drugs were achiral, whereas 27.6% (n=109) were chiral mixtures and 20.0% (n=79) were pure enantiomers. The mean duration on the market was 24.6±27.5 years. The groups did not differ in terms of mean duration on the market. Chiral mixtures were significantly more withdrawn than were achirals in cardiovascular system drugs (17.4% vs. 7.7%, p=0.01). In musculoskeletal system drugs, pure enantiomers were significantly less withdrawn (2.5%) compared to achirals (12.6%, p=0.01) and chiral mixtures (11.9%, p=0.03). Hepatotoxicity was significantly less common in pure enantiomers (5.4%) compared to chiral mixtures (12.7%, p=0.04) and achirals (17.0%, p&lt;0.01). Cardiovascular toxicity was significantly more common in chiral mixtures (14.5%) compared to that in achiral drugs (7.5%, p=0.02). Conclusion: Our study showed slightly higher representation of chiral mixtures among withdrawn drugs over pure enantiomers. The assessment of withdrawal reasons further indicates higher tendency of chiral mixtures towards hepatotoxicity and cardiovascular toxicity. Enantiomer ; Pharmacodynamics ; Chirality (physics) ; Pharmacokinetics ; Drug ; Pharmacology ; Adverse effect ; Chemistry ; Medicine ; Stereochemistry ; Chiral symmetry ; Physics Analytical Chemistry and Chromatography ; Pharmacogenetics and Drug Metabolism ; Chemical Reactions and Isotopes
Authors: Jakob K. Staab | Nicholas F. Chilton
Source: ChemRxiv (preprint, 2022-06-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An analytic linear vibronic coupling method for first-principles spin-dynamics calculations in single-molecule magnets The accurate modeling of vibronically driven magnetic relaxation from ab initio calculations is of paramount importance to the design of next generation single-molecule magnets (SMMs). Previous theoretical studies have been relying on numerical differentiation to obtain spin-phonon couplings in the form of derivatives of spin Hamiltonian parameters. In this work, we introduce a novel approach to obtain these derivatives fully analytically by combining the linear vibronic coupling (LVC) approach with analytic CASSCF derivatives and nonadiabatic couplings computed at a single equilibrium geometry and electronic structure. We apply our analytic approach to the computation of Orbach and Raman relaxation rates in a solvated bis-cyclobutadienyl Dy(III) sandwich complex, where the solution environment is modelled by electrostatic multipole expansions, and benchmark our findings against results obtained using conventional numerical derivatives and a fully electronic description of the whole system. Among the ghost, charge and charge+dipole representations, we find that the point charge model shows the best agreement with the reference calculation, but note that the main source of discrepancies observed in the magnetic relaxation rates originates from the approximate equilibrium electronic structure computed using the electrostatic environment models, rather than from the couplings. We demonstrate that our novel LVC approach exhibits high accuracy over a wide range of coupling strengths and enables computational savings due to its analytic, "single-shot'' nature. Evidently, this offers great potential for advancing the simulation of a wide range of vibronic coupling phenomena in magnetism and spectroscopy, ultimately aiding the design of high-performance SMMs. Hamiltonian (control theory) ; Relaxation (psychology) ; Vibronic coupling ; Coupling (piping) ; Multipole expansion ; Dipole ; Electronic structure ; Physics ; Statistical physics ; Chemistry ; Quantum mechanics ; Molecule ; Materials science ; Mathematics Magnetism in coordination complexes ; Porphyrin and Phthalocyanine Chemistry ; Electron Spin Resonance Studies