jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haixia Zhang
Source: Iranian journal of pharmaceutical research (article, 2024-12-25)
Field: Chemistry — Organic Chemistry

Exploring Herbal Compounds as Targeted Therapies for Breast Cancer: Insights from Network Pharmacology, Molecular Docking, MD Simulation, ADME-Toxicity and DFT Profiles The study provides insights into the implications of herbal compounds as targeted therapy for breast cancer. Therefore, the study recommends further experimental validation and development of herbal-based compounds for the treatment of breast cancer. ADME ; Breast cancer ; Docking (animal) ; Pharmacology ; Systems pharmacology ; Computational biology ; Medicine ; Cancer ; Traditional medicine ; Pharmacokinetics ; Bioinformatics ; Biology ; Drug ; Internal medicine Synthesis and biological activity ; Computational Drug Discovery Methods ; Cancer Mechanisms and Therapy
Authors: I Gusti Ayu Tirtayani | I Made Wardana | Putu Yudi Setiawan | I Gusti Ngurah Jaya Agung Widagda K
Source: JPM - Journal of Perspectives in Management (article, 2024-11-29)
Field: Chemistry — Organic Chemistry

scoping review This research conducts a scoping review on the application of machine learning (ML) in social media marketing, an increasingly pivotal area in digital marketing strategies. Machine learning plays a crucial role in analyzing user data, identifying consumer behavior, personalizing content, and optimizing marketing campaigns. Through a systematic search of academic journals, conference proceedings, and other relevant sources, this review identifies and synthesizes studies that explore the use of ML in social media marketing. The selected studies are analyzed to provide a comprehensive overview of ML's impact and applications within this domain. Key findings highlight the significant role of ML in enhancing personalization, improving user engagement, and driving more effective marketing strategies. However, challenges such as data privacy concerns, algorithmic biases, and the need for greater transparency are also noted. The practical implications for marketers include the importance of ethical practices in data handling, algorithm development, and consumer trust-building. Additionally, this review identifies gaps in the current literature and suggests directions for future research, offering valuable insights for both researchers and practitioners aiming to leverage machine learning in social media marketing. Computer science Chemistry and Stereochemistry Studies
Authors: Raluca Elena Irimescu | Doina Răducanu | Anna Nocivin | Elisabeta Mirela Cojocaru | Vasile Dănuț Cojocaru | Nicoleta Zărnescu-Ivan
Source: Materials (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Optimizing Suitable Mechanical Properties for a Biocompatible Beta-Titanium Alloy by Combining Plastic Deformation with Solution Treatment The microstructural and mechanical features were investigated for the alloy Ti-36.5Nb-4.5Zr-3Ta-0.16O (wt.%) subjected to thermo-mechanical processing consisting of a series of hot and cold rolling combined with solution treatments with particular parameters. The objective was to find the optimal thermo-mechanical treatment variant to improve the mechanical properties, and namely, to increase the yield tensile strength (YTS) and the ultimate tensile strength (UTS), with a low modulus of elasticity and with an adequate ductility in order to obtain a good biomaterial appropriate for use in hard tissue implants. X-ray diffraction and SEM microscopy served to investigate the microstructural features: the type of formed phases with their morphology, dimensions, and distribution. The experimental alloy presented mainly a β-phase with some α″-Ti martensitic phase in particular stages of the processing scheme. The main mechanical properties were found by applying a tensile test, from which were determined the yield tensile strength [MPa], the ultimate tensile strength [MPa], Young's modulus of elasticity [GPa], and the elongation to fracture (%). Materials science ; Ultimate tensile strength ; Alloy ; Titanium alloy ; Composite material ; Ductility (Earth science) ; Tensile testing ; Elastic modulus ; Young's modulus ; Microstructure ; Biomaterial ; Modulus ; Nanotechnology ; Creep Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Additive Manufacturing Materials and Processes
Authors: Jeramie I. Abadies | Hazel Mae P. Romulo | Edwin R. Arboleda | Judy Lhyn Porlaje Sarmiento
Source: Preprints.org (preprint, 2024-05-06)
Field: Chemistry — Analytical Chemistry

Coconut Maturity Detector Using Spectrogram and Convolutional Neural Network Coconut is an important multipurpose crop wherein different stages of its maturity are used for different products. Traditional approach of its classification use sound formed by tapping the fruit, and the tapper will base its judgment through the sound he heard. The procedure was highly subjective and attempts had been made by other researchers to automate and classify the adulthood of the coconut objectively. Nowadays, deep learning techniques are being utilized to solve classification problems. One such method is Convolutional Neural Network (CNN) that takes raw pixel data, learns to extract features, and ultimately classifies the input. In this study, a portable device was developed that takes audio signals generated from tapping the fruit mechanically. This audio clip was converted to spectrogram and was used as input to a LeNet CNN architecture. We argue that CNN classifier has improved accuracy compared to the non-deep learning system. Our evaluation confirmed that the use of CNN had improved the accuracy of classifying coconut by about 15%. Spectrogram ; Convolutional neural network ; Maturity (psychological) ; Detector ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Psychology ; Telecommunications ; Developmental psychology Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Usman Tariq | Fahad Saeed
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-08-22)
Field: Chemistry — Spectroscopy

Predicting peptide properties from mass spectrometry data using deep attention-based multitask network and uncertainty quantification Database search algorithms reduce the number of potential candidate peptides against which scoring needs to be performed using a single (i.e. mass) property for filtering. While useful, filtering based on one property may lead to exclusion of non-abundant spectra and uncharacterized peptides - potentially exacerbating the <i>streetlight</i> effect. Here we present <i>ProteoRift</i>, a novel attention and multitask deep-network, which can <i>predict</i> multiple peptide properties (length, missed cleavages, and modification status) directly from spectra. We demonstrate that <i>ProteoRift</i> can predict these properties with up to 97% accuracy resulting in search-space reduction by more than 90%. As a result, our end-to-end pipeline is shown to exhibit 8x to 12x speedups with peptide deduction accuracy comparable to algorithmic techniques. We also formulate two uncertainty estimation metrics, which can distinguish between in-distribution and out-of-distribution data (ROC-AUC 0.99) and predict high-scoring mass spectra against correct peptide (ROC-AUC 0.94). These models and metrics are integrated in an end-to-end ML pipeline available at https://github.com/pcdslab/ProteoRift. Mass spectrometry ; Computer science ; Chemistry ; Data mining ; Chromatography Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Metabolomics and Mass Spectrometry Studies
Authors: Jieye Lin | Guanhong Bu | Johan Unge | Tamir Gonen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-01-05)
Field: Chemistry — Physical and Theoretical Chemistry

Uncovering the Elusive Structures and Mechanisms of Prevalent Antidepressants Abstract Most treatments to alleviate major depression work by either inhibiting human monoamine transporters, vital for the reuptake of monoamine neurotransmitters, or by inhibiting monoamine oxidases, which are vital for their degradation. The analysis of the experimental 3D structures of those antidepressants in their drug formulation state is key to precision drug design and development. In this study, we apply microcrystal electron diffraction (MicroED) to reveal the atomic 3D structures for the first time of five of the most prevalent antidepressants (reboxetine, pipofezine, ansofaxine, phenelzine, bifemelane) directly from the commercially available powder of the active ingredients. Their modes of binding are investigated by molecular docking, revealing the essential contacts and conformational changes into the biologically active state. This study underscores the combined use of MicroED and molecular docking to uncover elusive drug structures and mechanisms to aid in further drug development pipelines. Monoamine neurotransmitter ; Phenelzine ; Reboxetine ; Drug ; Pharmacology ; Chemistry ; Monoamine oxidase ; Computational biology ; Reuptake inhibitor ; Medicine ; Biology ; Biochemistry ; Serotonin ; Enzyme Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Byran J. Smucker | Stephen E. Wright | Isaac Williams | Richard C. Page | Andor J. Kiss | Surendra Bikram Silwal | Maria L. Weese | D.J. Edwards
Source: arXiv (Cornell University) (preprint, 2024-07-08)
Field: Materials Science — Materials Chemistry

Large Row-Constrained Supersaturated Designs for High-throughput Screening High-throughput screening, in which multiwell plates are used to test large numbers of compounds against specific targets, is widely used across many areas of the biological sciences and most prominently in drug discovery. We propose a statistically principled approach to these screening experiments, using the machinery of supersaturated designs and the Lasso. To accommodate limitations on the number of biological entities that can be applied to a single microplate well, we present a new class of row-constrained supersaturated designs. We develop a computational procedure to construct these designs, provide some initial lower bounds on the average squared off-diagonal values of their main-effects information matrix, and study the impact of the constraint on design quality. We also show via simulation that the proposed constrained row screening method is statistically superior to existing methods and demonstrate the use of the new methodology on a real drug-discovery system. Throughput ; Computer science ; Parallel computing ; Operating system Machine Learning in Materials Science
Authors: Yuhan Peng | Jiaxu Xia | Qingxiang Li | Yiming Bi | Shitou Li | Hui Wang
Source: ACS Omega (article, 2024-11-26)
Field: Chemistry — Analytical Chemistry

Development and Application of a Quantitative Model for Proximate and Ultimate Analysis of Flue-Cured Tobacco Based on Near-Infrared Spectroscopy A methodology for predicting proximate and ultimate analysis data was developed by using near-infrared spectroscopy (NIR) combined with chemometric methods. The quantitative model has high accuracy, as evidenced by low root-mean-square-error of prediction (RMSEP) values (e.g., 0.41% for volatile matter and 0.29% for carbon). The model was further applied to tobaccos with distinct aroma profiles, and the predicted ultimate and proximate data lead to aroma classification with 86.6% accuracy. This methodology can be expanded to the aroma discrimination of imported tobaccos from Brazil, the United States, Canada, and Zimbabwe, demonstrating its broad reliability. Compared with traditional analyses, this NIR-based approach offers a fast and accurate method for large-scale tobacco evaluation, highlighting its potential for enhancing tobacco quality characterization through a quantifiable, digital, and high-throughput process. Aroma ; Near-infrared spectroscopy ; Curing of tobacco ; Reliability (semiconductor) ; Proximate ; Food science ; Environmental science ; Chemistry ; Botany ; Power (physics) ; Psychology ; Biology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Listeria monocytogenes in Food Safety
Authors: Minghu Zhao | Dashuai Wang | Qing Yan | Zhuolin Li | Xiaoguang Liu
Source: Agriculture (article, 2024-12-26)
Field: Chemistry — Analytical Chemistry

UAV-Multispectral Based Maize Lodging Stress Assessment with Machine and Deep Learning Methods Maize lodging is a prevalent stress that can significantly diminish corn yield and quality. Unmanned aerial vehicles (UAVs) remote sensing is a practical means to quickly obtain lodging information at field scale, such as area, severity, and distribution. However, existing studies primarily use machine learning (ML) methods to qualitatively analyze maize lodging (lodging and non-lodging) or estimate the maize lodging percentage, while there is less research using deep learning (DL) to quantitatively estimate maize lodging parameters (type, severity, and direction). This study aims to introduce advanced DL algorithms into the maize lodging classification task using UAV-multispectral images and investigate the advantages of DL compared with traditional ML methods. This study collected a UAV-multispectral dataset containing non-lodging maize and lodging maize with different lodging types, severities, and directions. Additionally, 22 vegetation indices (VIs) were extracted from multispectral data, followed by spatial aggregation and image cropping. Five ML classifiers and three DL models were trained to classify the maize lodging parameters. Finally, we compared the performance of ML and DL models in evaluating maize lodging parameters. The results indicate that the Random Forest (RF) model outperforms the other four ML algorithms, achieving an overall accuracy (OA) of 89.29% and a Kappa coefficient of 0.8852. However, the maize lodging classification performance of DL models is significantly better than that of ML methods. Specifically, Swin-T performs better than ResNet-50 and ConvNeXt-T, with an OA reaching 96.02% and a Kappa coefficient of 0.9574. This can be attributed to the fact that Swin-T can more effectively extract detailed information that accurately characterizes maize lodging traits from UAV-multispectral data. This study demonstrates that combining DL with UAV-multispectral data enables a more comprehensive understanding of maize lodging type, severity, and direction, which is essential for post-disaster rescue operations and agricultural insurance claims. Multispectral image ; Agronomy ; Agricultural engineering ; Stress (linguistics) ; Environmental science ; Remote sensing ; Artificial intelligence ; Computer science ; Agroforestry ; Biology ; Geography ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Jakub Bańczerowski | Marek Pawlikowski | Tomasz Płociński | A. Zagórski | S. Sawicki | Roman Gieleta
Source: Continuum Mechanics and Thermodynamics (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

New approach to $${\upalpha }$$-titanium mechanical properties enhancement by means of thermoplastic deformation in mid-temperature range Abstract Pure titanium due to its high corrosion resistance, low stiffness and good mechanical properties is commonly used in medicine for orthopaedic applications. However, its material properties (especially in the case of $${\upalpha }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> -titanium) require a further enhancement to fulfil its role. The thermoplastic deformation in mid-temperature is proposed as a method for microstructure improvement. Titanium samples were compressed in different temperatures and strain rates to determine the best conditions for grain fragmentation—the main factor responsible for strength and hardness increase. The thermoplastic stress–strain curves were registered. Then microstructure observations and electron backscatter analysis were performed on the chosen samples. Finally, mechanical response of the previously deformed material was obtained in room temperature compression tests. A significant grain fragmentation was recorded for the material deformed in 400 $$^{\circ }\hbox {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mmultiscripts> <mml:mrow/> <mml:mrow/> <mml:mo>∘</mml:mo> </mml:mmultiscripts> <mml:mtext>C</mml:mtext> </mml:mrow> </mml:math> , at 0.1/s and 1/s strain rates. Desirable results were also noticed for the deformation performed at 500–600 $$^{\circ }\hbox {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mmultiscripts> <mml:mrow/> <mml:mrow/> <mml:mo>∘</mml:mo> </mml:mmultiscripts> <mml:mtext>C</mml:mtext> </mml:mrow> </mml:math> . However, high temperatures (700–800 $$^{\circ }\hbox {C}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mmultiscripts> <mml:mrow/> <mml:mrow/> <mml:mo>∘</mml:mo> </mml:mmultiscripts> <mml:mtext>C</mml:mtext> </mml:mrow> </mml:math> ) and strain rates (10/s) resulted in dynamic recrystallization, causing undesirable grain growth. An increase in hardness was observed in all cases, with higher values recorded in lower deformation temperatures. Room temperature compression tests revealed slight increase of ductility. Structural material ; Materials science ; Deformation (meteorology) ; Range (aeronautics) ; Titanium ; Composite material ; Atmospheric temperature range ; Thermoplastic ; Mechanical engineering ; Metallurgy ; Thermodynamics ; Engineering ; Physics Titanium Alloys Microstructure and Properties ; Microstructure and mechanical properties ; Advanced materials and composites
Authors: T. Narendiranath Babu | Suraj Shyam | D. Rama Prabha | Shivam Kaul | Nirav Kalsara
Source: International Journal of Automotive and Mechanical Engineering (article, 2024-09-20)
Field: Materials Science — Polymers and Plastics

Investigation of the Mechanical Properties of Hybrid E-Glass and Mohair Fiber Reinforced Epoxy Composites Natural fiber composites have significant potential to replace traditional materials used in industries due to their excellent tensile strength, stiffness, low specific weight, and superior thermal and insulating properties. Mohair fiber, also known as the Noble fiber and the Diamond fiber, is obtained from the Angora goat, an animal of Tibetan origin. Renowned for its brilliant luster and resilience, Mohair is a symbol of luxury and exclusivity. The primary objective of this study is to manufacture and test a new natural fiber composite that could potentially outperform existing materials in real-world applications. Specifically, the study aims to investigate the mechanical properties of this composite. Mohair has previously been identified as a fiber many times stronger than a strand of steel of similar dimensions. Incorporating these highly desirable properties of Mohair into an epoxy matrix is one of the novel aspects of this research. The testing procedure begins with the preparation of ASTM molds, concurrent treatment of the fibers, and the preparation of the binding material. Specimens are created both with and without the addition of electrical glass (E- glass) fibers. The next phase is curing, during which the epoxy is allowed to solidify, forming strong bonds. Once developed and cured, the composites are removed from their molds and undergo post-processing and finishing techniques. This study aims to understand the tensile and flexural characteristics of natural fiber composites reinforced with epoxy. Three fiber orientations—uniaxial, biaxial, and criss-cross—are employed to assess the changes in the mechanical properties of the composites. The results indicate that adding E-glass fibers in alternating layers of the composite significantly enhances both tensile and flexural strength. Statistically, the addition of the biaxial arrangement of fibers with E-glass fibers results in an 18.47% improvement in ultimate tensile strength, while the maximum flexural strength increases by 49.90%. Furthermore, the topography of the cracked surface is examined using field emission scanning electron microscopy, and the breaking of the fibers in all three directions is studied. Materials science ; Composite material ; Epoxy ; Ultimate tensile strength ; Glass fiber ; Curing (chemistry) ; Flexural strength ; Composite number ; Fiber ; Natural fiber ; Specific strength Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Tribology and Wear Analysis
Authors: Hari P. Upadhyaya
Source: International Journal of Quantum Chemistry (article, 2024-06-03)
Field: Chemistry — Physical and Theoretical Chemistry

Theoretical studies on the kinetics and mechanism of hydroxyl radical reaction with quinclorac and quinmerac herbicides in aqueous media Abstract The kinetic and mechanistic studies for the reaction of hydroxyl radical with two quinoline based herbicides, namely, quinclorac and quinmerac has been performed using various computational methods in aqueous media. Geometry optimizations were performed using Density Functional Theory (DFT) methods including water as the solvent. Local reactivity parameters of these herbicides towards the • OH radical are predicted using condensed Fukui function. Single point energies of various species were calculated using double hybrid method, namely, B2PLYP–D for better accuracy. The pK a values for these acid based herbicides allow them to exist in deprotonated form in aqueous condition. Hence, the calculations are also performed for the deprotonated or the anionic form apart from the neutral species. Individual rate coefficients for • OH radical addition reaction with each carbon atoms were evaluated using conventional transition state theory using one–dimensional tunneling corrections. The solvent effect on reaction is implemented through Collins–Kimball formulations. Both the approaches, namely, the Fukui index and individual rate constant determination confirms that the most reactive site for the • OH radical addition in these two herbicide is the carbon atom attached to the COOH group. The total rate constant for the • OH radical reaction with both neutral and anionic forms of these two herbicides are relatively high and equal to its diffusion‐limit value. Evaluation of the ecotoxicities of the parent herbicides and their OH adducts is estimated using the structure–activity relationship concept. Chemistry ; Kinetics ; Aqueous medium ; Hydroxyl radical ; Photochemistry ; Aqueous solution ; Mechanism (biology) ; Computational chemistry ; Radical ; Organic chemistry Photochemistry and Electron Transfer Studies ; Advanced oxidation water treatment ; Spectroscopy and Quantum Chemical Studies