jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Baydaa Sulaiman Bahnam | Nour Abd AL Khaliq Fadel
Source: Mağallaẗ al-tarbiyaẗ wa-al-ʻilm (article, 2024-02-29)
Field: Chemistry — Analytical Chemistry

Determining the Quality of Dairy Products Using Machine Learning Techniques Food products are an essential source of human life, so they have a very important place, and it will be important to monitor and determine their quality in a short time. Our study deals with a very important and indispensable food product, which is the milk product, which is considered the main and important element in people’s lives, especially children, because it is the main source of their growth, building their bones, and strengthening. Because it is a highly perishable product, it must be monitored and its specifications must be monitored, because any gram of milk that is of low or poor quality may cause tons of milk to spoil, and also cause major financial losses. Therefore, a study was conducted to determine the quality of dairy product through machine learning algorithms (ML), which are support vector machine (SVM) algorithm, nearest neighbors (KNN) algorithm, decision tree (DT) algorithm and Bagging algorithm using milk dataset taken from data warehouse Kaggle. This data consists of 1059 samples and seven features. The proposed models were trained and tested with the aim of finding the best and most accurate model for detecting milk quality and were evaluated using the evaluation metrics: accuracy, precision, recall, f1_score and confusion matrix. According to the evaluation results three models: SVM, KNN, and DT outperformed Bagging algorithm, as they obtained the highest level for all metrics 100%. The SVM algorithm was the most efficient because its execution time was 0.146 seconds, which was less than the other models. Quality (philosophy) ; Computer science ; Machine learning ; Artificial intelligence ; Biochemical engineering ; Engineering ; Physics Spectroscopy and Chemometric Analyses
Authors: Mariya Nezhyva | Jordan T. Aerts | Friederike A. Sandbaumhüter | Theodosia Vallianatou | Erik T. Jansson
Source: Journal of Chemical Education (article, 2024-01-27)
Field: Chemistry — Spectroscopy

Inquiry-based Learning of Proteomics and Metabolomics Given the rapid development of data-driven experimental procedures within the life sciences, it is important to equip students with proper skills and knowledge on how to obtain and interpret complex data. While laboratory exercises have for a long time been well established as a teaching method for life sciences, we consider there is room for improvement in how laboratory exercises are conducted. Hence, we designed a laboratory exercise course in which students at the graduate level in the European education system (M.Sc.) are challenged to pose their own biological question and write their own laboratory protocol for a proteomic study to investigate their hypothesis. Here, students are supported in their task with lectures and seminars that take them through the required details on experimental sample preparation, analysis with LC–MS, and proteomic data evaluation of biological function and relevance. According to student interviews, the inquiry-based learning concept we used here provided a deeper understanding of the laboratory protocols they wrote, according to which they eventually performed their own experiments. Protocol (science) ; Relevance (law) ; Task (project management) ; Computer science ; Graduate students ; Mathematics education ; Function (biology) ; Biological sciences ; Data science ; Undergraduate research ; Psychology ; Medical education ; Computational biology ; Medicine ; Pedagogy ; Biology ; Engineering Advanced Proteomics Techniques and Applications ; Various Chemistry Research Topics ; Metabolomics and Mass Spectrometry Studies
Authors: Jayshri Pawar | Ravindra R. Malagi | Manoj M. Jadhav | Girish B. Pawar
Source: Discover Applied Sciences (article, 2024-12-28)
Field: Materials Science — Polymers and Plastics

Enhanced biodegradability and mechanical properties in green composites: a Taguchi approach Global government restrictions, such as carbon–neutral aims and increased environmental awareness, drive sustainable material development. In this context, the work applied the Taguchi technique to identify the parameters that significantly affected the green composite’s mechanical performance and biodegradability. Flax and sisal fiber mats are the reinforcing materials. We combined polylactic acid (PLA) granules with polybutylene succinate (PBS) for development in 20 and 30 percentage variations to enhance properties. Minitab 17.0 was used to create the L8 orthogonal matrix for experimentation. The effect of control parameters was analyzed to examine the best composite specification. The contribution of control parameters and the confirmation test was studied using analysis of variance (ANOVA). This composite has 49.8 MPa tensile strength, 97 MPa flexural strength, and 77 J impact energy absorbed. The flax specimen has the lowest moisture absorption at 17%. This study recommends the flax fiber mat-reinforced PLA/PBS green composite used in secondary load-bearing structural applications, such as automobile components and building panels. Taguchi methods ; Biodegradation ; Composite material ; Materials science ; Chemistry ; Organic chemistry Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; biodegradable polymer synthesis and properties
Authors: Yashaswini Mallepura Adinarayanaswamy | Deepthi Padmanabhan | Purushothaman Natarajan | S PALANISAMY
Source: Pharmaceuticals (article, 2024-03-26)
Field: Materials Science — Biomaterials

Metabolomic Profiling of Leptadenia reticulata: Unveiling Therapeutic Potential for Inflammatory Diseases through Network Pharmacology and Docking Studies Medicinal plants have been utilized since ancient times for their therapeutic properties, offering potential solutions for various ailments, including epidemics. Among these, <i>Leptadenia reticulata</i>, a member of the <i>Asclepiadaceae</i> family, has been traditionally employed to address numerous conditions such as diarrhea, cancer, and fever. In this study, employing HR-LCMS/MS(Q-TOF) analysis, we identified 113 compounds from the methanolic extract of <i>L. reticulata</i>. Utilizing Lipinski's rule of five, we evaluated the drug-likeness of these compounds using SwissADME and ProTox II. SwissTarget Prediction facilitated the identification of potential inflammatory targets, and these targets were discerned through the Genecard, TTD, and CTD databases. A network pharmacology analysis unveiled hub proteins including CCR2, ICAM1, KIT, MPO, NOS2, and STAT3. Molecular docking studies identified various constituents of <i>L. reticulata</i>, exhibiting high binding affinity scores. Further investigations involving in vivo testing and genomic analyses of metabolite-encoding genes will be pivotal in developing efficacious natural-source drugs. Additionally, the potential of molecular dynamics simulations warrants exploration, offering insights into the dynamic behavior of protein-compound interactions and guiding the design of novel therapeutics. Profiling (computer programming) ; Docking (animal) ; Pharmacology ; Medicine ; Computational biology ; Biology ; Veterinary medicine ; Computer science Phytochemistry and Bioactive Compounds ; Microbial Natural Products and Biosynthesis ; Fungal Biology and Applications
Authors: Martin Kubáň | Santiago Rigamonti | Claudia Draxl
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

MADAS: a Python framework for assessing similarity in materials-science data MADAS is a computational framework that supports all steps of similarity analysis, including the collection and storage of data, the development and computation of fingerprints, metrics to measure similarity, and data analytics and machine learning. Python (programming language) ; Computer science ; Similarity (geometry) ; Information retrieval ; Data science ; Artificial intelligence ; Programming language Machine Learning in Materials Science
Authors: Jeheon Woo | Woo Youn Kim | Sunghwan Choi
Source: Journal of Chemical Theory and Computation (article, 2024-08-27)
Field: Chemistry — Spectroscopy

Efficient Shift-and-Invert Preconditioning for Multi-GPU Accelerated Density Functional Calculations To accelerate the iterative diagonalization of electronic structure calculations, we propose a new inexact shift-and-invert (ISI) preconditioning method. The key idea is to improve shift values in the ISI preconditioning to be closer to the exact eigenvalues, leading to a significant boost in the convergence speed of the iterative diagonalization. Furthermore, we adopted a preconditioned conjugate gradient solver to rapidly evaluate an inversion process. Finally, we accelerated overall processes, including the proposed modification, with state-of-the-art graphical processing units (GPUs) and assessed its parallel efficiency with real-space density functional calculations of 1D, 2D, and 3D periodic systems. Our method attains both fast diagonalization convergence and high multi-GPU parallel efficiency. This is evident from the fact that single-point density functional calculations for hundreds of atom systems can be done in approximately 10 s using 8 GPUs. The proposed method can be generally applied to any electronic structure calculation methods involving large-scale diagonalizations. Conjugate gradient method ; Computer science ; Solver ; Convergence (economics) ; Computational science ; Eigenvalues and eigenvectors ; Iterative method ; Algorithm ; Parallel computing ; Physics Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography ; Matrix Theory and Algorithms
Authors: Gang Xiao | Xiaolei Zhang | Siqi Wang | Shixin Lai | Tingting Nie | Yaowen Chen | Caiyu Zhuang | Gen Yan | Renhua Wu
Source: Scientific Reports (article, 2024-09-14)
Field: Materials Science — Materials Chemistry

Quantitative separation of CEST effect by Rex-line-fit analysis of Z-spectra The process of chemical exchange saturation transfer (CEST) is quantified by evaluating a Z-spectra, where CEST signal quantification and Z-spectra fitting have been widely used to distinguish the contributions from multiple origins. Based on the exchange-dependent relaxation rate in the rotating frame (R<sub>ex</sub>), this paper introduces an additional pathway to quantitative separation of CEST effect. The proposed R<sub>ex</sub>-line-fit method is solved by a multi-pool model and presents the advantage of only being dependent of the specific parameters (solute concentration, solute-water exchange rate, solute transverse relaxation, and irradiation power). Herein we show that both solute-water exchange rate and solute concentration monotonously vary with R<sub>ex</sub> for Amide, Guanidino, NOE and MT, which has the potential to assist in solving quantitative separation of CEST effect. Furthermore, we achieve R<sub>ex</sub> imaging of Amide, Guanidino, NOE and MT, which may provide direct insight into the dependency of measurable CEST effects on underlying parameters such as the exchange rate and solute concentration, as well as the solute transverse relaxation. Separation (statistics) ; Line (geometry) ; Computational biology ; Computer science ; Chemistry ; Biology ; Mathematics ; Machine learning Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Medical Imaging Techniques and Applications
Authors: S. Schmidt | Alexander Geisel | Thomas Enzlein | Björn Fröhlich | Louise Pritchett | Mélanie Verneret | C. Graf | Carsten Hopf
Source: Scientific Reports (article, 2024-09-13)
Field: Chemistry — Spectroscopy

Label-free assessment of complement-dependent cytotoxicity of therapeutic antibodies via a whole-cell MALDI mass spectrometry bioassay Potency assessment of monoclonal antibodies or corresponding biosimilars in cell-based assays is an essential prerequisite in biopharmaceutical research and development. However, cellular bioassays are still subject to limitations in sample throughput, speed, and often need costly reagents or labels as they are based on an indirect readout by luminescence or fluorescence. In contrast, whole-cell Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) Mass Spectrometry (MS) has emerged as a direct, fast and label-free technology for functional drug screening being able to unravel the molecular complexity of cellular response to pharmaceutical reagents. However, this approach has not yet been used for cellular testing of biologicals. In this study, we have conceived, developed and benchmarked a label-free MALDI-MS based cell bioassay workflow for the functional assessment of complement-dependent cytotoxicity (CDC) of Rituximab antibody. By computational evaluation of response profiles followed by subsequent m/z feature annotation via fragmentation analysis and trapped ion mobility MS, we identified adenosine triphosphate and glutathione as readily MS-assessable metabolite markers for CDC and demonstrate that robust concentration-response characteristics can be obtained by MALDI-TOF MS. Statistical assay performance indicators suggest that whole-cell MALDI-TOF MS could complement the toolbox for functional cellular testing of biopharmaceuticals. Chemistry ; Bioassay ; Mass cytometry ; Mass spectrometry ; Computational biology ; Biopharmaceutical ; Cytotoxicity ; Chromatography ; Biochemistry ; In vitro ; Biology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Erick A. Adjé | Gilles Delmaire | Arnaud Ahouandjinou | Matthieu Puigt | Gilles Roussel
Source:  (article, 2024-12-09)
Field: Chemistry — Analytical Chemistry

Robust Approach for Butterfly Species Classification Using a Single Spatio-Hyperspectral Image Distinguishing between pest and pollinator butterfly species is a major challenge in precision agriculture. However, traditional RGB cameras, capturing only shape and surface color, are insufficient for detailed insect analysis. This work explores the rich spectral information provided by hyperspectral imaging for effective butterfly species identification. For this purpose, we use a single spatio-spectral image that provides partial spectral information to identify the butterfly species. The proposed classification approach consists of a convex combination of the probabilistic decisions obtained by the Gaussian Naive Bayes and Z-score methods for each butterfly reflectance. Compared to traditional classification models, this approach showed higher robustness and performance. Hyperspectral imaging ; Butterfly ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Contextual image classification ; Computer vision ; Image (mathematics) ; Ecology ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Date Palm Research Studies
Authors: Qiang Chen | Chuang Xia | Yinyan Shi | Xiaochan Wang | Xiaolei Zhang | Ye He
Source: Agronomy (article, 2024-05-30)
Field: Chemistry — Analytical Chemistry

A Novel Approach for Asparagus Comprehensive Classification Based on TOPSIS Evaluation and SVM Prediction As a common vegetable variety, asparagus is rich in B vitamins, vitamin A, and trace elements such as folate, selenium, iron, manganese, and zinc. With the increasing market demand, China has become the world’s largest cultivated area for asparagus production and product exportation. However, traditional asparagus grading mostly relies on manual visual judgment and needs a lot of manpower input to carry out the classification operation, which cannot meet the needs of large-scale production. To address the high labor cost and labor-intensive production process resulting from the large amount of manpower input and low accuracy of existing asparagus grading devices, this study proposed an improved asparagus grading system and method based on TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) objective evaluation and SVM (support vector machine) prediction. The key structure of classification device was analyzed first, the key components were designed, and the structural parameters were determined by theoretical calculation. Through analysis of the factors affecting asparagus quality, three key attributes were determined: length, diameter, and bruises, which were used as reference attributes to conduct experimental analysis. Then, the graded control groups were set up, combining the TOPSIS principle with weighting, and a score for each asparagus sample was determined. These scores were compared with those of a graded control group to derive the grade of each asparagus, and these subsets of the dataset were used as the training set and the test set, excluding the error caused by the subjectivity of the manual judgment. Based on a comparison of the accuracies of different machine learning models, the support vector machine (SVM) was determined to be the most accurate, and four SVM methods were used to evaluate the test set: linear SVM, quadratic SVM, cubic SVM, and medium Gaussian SVM. The test results showed that the grading device was feasible for asparagus. The bruises had a large influence on asparagus quality. The training accuracy of the medium Gaussian SVM method was high (96%), whereas its test accuracy was low (86.67%). The training accuracies and test accuracy of the quadratic and cubic SVM methods were 93.34%. The quadratic SVM and cubic SVM were demonstrated to have better generalization ability than the medium Gaussian SVM method for predicting unknown grades of asparagus and meeting the operational requirements of the asparagus grading. Asparagus ; TOPSIS ; Grading (engineering) ; Support vector machine ; Computer science ; Mathematics ; Agricultural engineering ; Statistics ; Artificial intelligence ; Operations research ; Horticulture ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use
Authors: Martha Elena Ibargüengoitia
Source: Chemistry Teacher International (article, 2024-12-12)
Field: Chemistry — Physical and Theoretical Chemistry

pH scale. An experimental approach to the math behind the pH chemistry Abstract We present an experimental activity designed for upper secondary students, where a pH scale is constructed taking advantage of the ability of a pH indicator to display different colors according to the pH of the medium. To build the scale, two solutions are prepared: 1 M HCl (pH = 0) and 1 M NaOH (pH = 14). Each original solution undergoes six consecutive 1/10 dilutions, producing acidic solutions with pH values of 1, 2, 3, 4, 5, and 6; and basic solutions with pH values of 13, 12, 11, 10, 9, and 8. Pure water (nominal pH = 7) serves as the reference. Upon adding the indicator, a beautiful rainbow of colors appears in the solution containers. To connect the experimental results with its mathematical representation, a written exercise is provided for students. This activity allows them to visually understand that, even though the change in pH is only one unit, the change in H + concentration is 10 times greater or smaller. Thus, pH is an exponential function, best expressed in logarithmic terms. Scale (ratio) ; Chemistry ; Mathematics education ; Mathematics ; Physics Various Chemistry Research Topics ; Science Education and Pedagogy
Authors: A. Arun | V. Sampath | C. Balasubramaniyan | A. Nair | R. Sabarish | P. V. Shravan | A. Vigneshwar
Source:  (article, 2024-02-05)
Field: Materials Science — Materials Chemistry

Development of a Shape-Memory-Alloy-Based Overheating Protection System Shape Memory Alloys (SMAs) are a class of metallic alloys that have the ability to return to their original shape after being deformed. NiTi (nickel–titanium) alloy a type of shape memory alloy that possesses unique properties such as remembering its shape, biocompatibility, and super-elasticity. These SMAs have the ability to deform when heated and regain their original shape when allowed to cool. The disadvantage of a fuse is that it can only be used once. By replacing a wire that melts with a NiTi shape memory alloy, we can turn it into a switch that opens when an excessive current (which leads to an increase in temperature of the wire) is applied. Magnets keep the circuit closed. When the wire heats up, the spring-shaped coil shrinks, which opens the circuit. The circuit can then be closed manually once the problems are rectified. Shape-memory alloy ; Nickel titanium ; Overheating (electricity) ; Materials science ; Electromagnetic coil ; Alloy ; Fuse (electrical) ; Coil spring ; Mechanical engineering ; Metallurgy ; Electrical engineering ; Spring (device) ; Engineering Shape Memory Alloy Transformations
Authors: Trenton M. Peters-Clarke | Joshua J. Coon | Nicholas M. Riley
Source: ChemRxiv (preprint, 2024-04-21)
Field: Chemistry — Spectroscopy

Instrumentation at the leading edge of proteomics The proteome, or collection of proteoforms expressed in a biological system, is dynamic and heterogeneous. As our appreciation for the complexity of the proteome has evolved, so have the technologies we use to interrogate its composition. More than three decades ago, a rapid expansion in the field of proteomics was driven by the advent of soft ionization techniques focusing on capturing protein sequence information using mass spectrometry (MS). As tools to automate peptide and protein sequencing with tandem MS (MS/MS) matured, our field recognized the limits of qualitatively cataloguing gene products. This realization drove a multi-pronged expansion of MS-centric technologies that seek to capture various aspects of proteins, including their abundance, modification states, conformation and structure, and spatiotemporal relationships. Here we review innovations in MS-based instrumentation that continue to expand our ability to survey the proteome with ever increasing sensitivity, speed, and flexibility. The march of progress in MS instrumentation has been steady over the better half of the past century, but our discussion here focuses on developments within the past five years that have ushered in an exciting era in proteomics, where MS is poised to be the dominant platform for exploring biological phenotypes in basic and translational sciences for the foreseeable future. Instrumentation (computer programming) ; Proteomics ; Enhanced Data Rates for GSM Evolution ; Computer science ; Data science ; Systems engineering ; Engineering ; Biology ; Artificial intelligence ; Programming language Advanced Proteomics Techniques and Applications