jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lala Septem Riza | M Ammar Fadhlur Rahman | Yudi Prasetyo | Muhammad Iqbal Zain | Herbert Siregar | Topik Hidayat | Khyrina Airin Fariza Abu Samah | Miftahurrahma Rosyda
Source: Knowledge Engineering and Data Science (article, 2023-11-28)
Field: Chemistry — Organic Chemistry

Comparison of Machine Learning Algorithms for Species Family Classification using DNA Barcode Classifying plant species within the Liliaceae and Amaryllidaceae families presents inherent challenges due to the complex genetic diversity and overlapping morphological traits among species. This study explores the difficulties in accurate classification by comparing 11 supervised learning algorithms applied to DNA barcode data, aiming to enhance the precision of species family classification in these taxonomically intricate plant families. The ribulose-1,5-bisphosphate carboxylase-oxygenase large sub-unit (rbcL) gene, selected as a DNA barcode locus for plants, is used to represent species within the Amaryllidaceae and Liliaceae families. The experimental results demonstrate that nearly all tested models achieve accurate species classification into the appropriate families, with an accuracy rate exceeding 97%, except for the Naïve Bayes model. Regarding computational time, the Random Forest model requires significantly more time for training than other models. Regarding memory usage, the Least Squares Support Vector Machine with a polynomial kernel, and Regularized Logistic Regression consume more memory than other models. These machine learning models exhibit strong concordance with NCBI's classifications when predicting families using the test dataset, effectively categorizing species into the Amaryllidaceae and Liliaceae families. Barcode ; Machine learning ; Artificial intelligence ; DNA barcoding ; Loo ; Naive Bayes classifier ; Support vector machine ; Liliaceae ; Random forest ; Biology ; Computer science ; Botany ; Ecology Chemical synthesis and alkaloids ; Genetic diversity and population structure ; Plant and animal studies
Authors: Isabella Marques de Carvalho | Yhan S. Mutz | Amanda Cristina Gomes Machado | Amanda Aparecida de Lima Santos | Elisângela Jaqueline Magalhães | Cleiton Antônio Nunes
Source: Foods (article, 2023-09-15)
Field: Chemistry — Analytical Chemistry

Exploring Strategies to Mitigate the Lightness Effect on the Prediction of Soybean Oil Content in Blends of Olive and Avocado Oil Using Smartphone Digital Image Colorimetry Extra virgin olive oil (EVOO) and avocado oil (AVO) are recognized for their unique sensory characteristics and bioactive compounds. Declared blends with other vegetable oils are legal, but undeclared mixing is a common type of fraud that can affect product quality and commercialization. In this sense, this study explored strategies to mitigate the influence of lighting in order to make digital image colorimetry (DIC) using a smartphone more robust and reliable for predicting the soybean oil content in EVOO and AVO blends. Calibration models were obtained by multiple linear regression using the images' RGB values. Corrections based on illuminance and white reference were evaluated to mitigate the lightness effect and improve the method's robustness and generalization capability. Lastly, the prediction of the built model from data obtained using a distinct smartphone was assessed. The results showed models with good predictive capacities, R<sup>2</sup> > 0.9. Generally, models solely based on GB values showed better predictive performances. The illuminance corrections and blank subtraction improved the predictions of EVOO and AVO samples, respectively, for image acquisition from distinct smartphones and lighting conditions as evaluated by external validation. It was concluded that adequate data preprocessing enables DIC using a smartphone to be a reliable method for analyzing oil blends, minimizing the effects of variability in lighting and imaging conditions and making it a potential technique for oil quality assurance. RGB color model ; Lightness ; Digital imaging ; Computer science ; Artificial intelligence ; Mathematics ; Digital image ; Process engineering ; Image processing ; Engineering ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Benjamin O. Tayo | Michael A. Walkup | Serkan Caliskan
Source: AIP Advances (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Adsorption of DNA nucleobases on single-layer Ti3C2 MXene and graphene: vdW-corrected DFT and NEGF studies We investigated the interaction of DNA nucleobases [adenine (A), guanine (G), thymine (T), and cytosine (C)] with single-layer Ti<sub>3</sub>C<sub>2</sub> MXene using Van der Waals (vdW)-corrected density functional theory and non-equilibrium Green's function methods. All calculations were benchmarked against graphene. We showed that depending on the initial vertical height of a nucleobase above the Ti<sub>3</sub>C<sub>2</sub> surface, two interaction mechanisms are possible, namely, physisorption and chemisorption. For graphene, DNA nucleobases always physisorbed onto the graphene surface irrespective of the initial vertical height of the nucleobase above the graphene sheet. The PBE+vdW binding energies for graphene are high (0.55-0.74 eV) and follow the order G > A > T > C, with adsorption heights in the range of 3.16-3.22 Å, indicating strong physisorption. For Ti<sub>3</sub>C<sub>2</sub>, the PBE+vdW binding energies are relatively weaker (0.16-0.20 eV) and follow the order A > G = T > C, with adsorption heights in the range of 5.51-5.60 Å, indicating weak physisorption. The binding energies for chemisorption follow the order G > A > T > C, which is the same order for physisorption. The binding energy values (5.3-7.5 eV) indicate very strong chemisorption (∼40 times larger than the physisorption binding energies). Furthermore, our band structure and electronic transport analysis showed that for physisorption, there is neither significant variation in the band structure nor modulation in the transmission function and device density of states. The relatively weak physisorption and strong chemisorption show that Ti<sub>3</sub>C<sub>2</sub> might not be capable of identifying DNA nucleobases using the physisorption method. Physisorption ; Chemisorption ; Nucleobase ; Graphene ; van der Waals force ; Binding energy ; Density functional theory ; Chemistry ; Adsorption ; Chemical physics ; Computational chemistry ; Materials science ; Physical chemistry ; Atomic physics ; Nanotechnology ; DNA ; Molecule ; Physics ; Organic chemistry MXene and MAX Phase Materials ; Graphene research and applications ; 2D Materials and Applications
Authors: Jiawei Xu | Chengdong Yu | Xiaoqiang Zeng | Weifeng Tang | Siyi Xu | Lei Tang | Yanxiao Huang | Zhengkui Sun | Tenghua Yu
Source: European journal of medical research (article, 2023-10-27)
Field: Chemistry — Spectroscopy

Visualization of breast cancer-related protein synthesis from the perspective of bibliometric analysis Breast cancer, as a daunting global health threat, has driven an exponential growth in related research activity in recent decades. An area of research of paramount importance is protein synthesis, and the analysis of specific proteins inextricably linked to breast cancer. In this article, we undertake a bibliometric analysis of the literature on breast cancer and protein synthesis, aiming to provide crucial insights into this esoteric realm of investigation. Our approach was to scour the Web of Science database, between 2003 and 2022, for articles containing the keywords "breast cancer" and "protein synthesis" in their title, abstract, or keywords. We deployed bibliometric analysis software, exploring a range of measures such as publication output, citation counts, co-citation analysis, and keyword analysis. Our search yielded 2998 articles that met our inclusion criteria. The number of publications in this area has steadily increased, with a significant rise observed after 2003. Most of the articles were published in oncology or biology-related journals, with the most publications in Journal of Biological Chemistry, Cancer Research, Proceedings of the National Academy of Sciences of the United States of America, and Oncogene. Keyword analysis revealed that "breast cancer," "expression," "cancer," "protein," and "translation" were the most commonly researched topics. In conclusion, our bibliometric analysis of breast cancer and related protein synthesis literature underscores the burgeoning interest in this research. The focus of the research is primarily on the relationship between protein expression in breast cancer and the development and treatment of tumors. These studies have been instrumental in the diagnosis and treatment of breast cancer. Sustained research in this area will yield essential insights into the biology of breast cancer and the genesis of cutting-edge therapies. Breast cancer ; Bibliometrics ; Citation ; Citation analysis ; Cancer ; Medicine ; Library science ; Computer science ; Internal medicine Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Cancer-related Molecular Pathways
Authors: Ranjitha Acharya | Shilpa S. Shetty | Pavan Gollapalli | Flama Monteiro | Manne Munikumar | Sriram Naresh | Suchetha Kumari
Source: Biomolecules (article, 2023-11-20)
Field: Chemistry — Spectroscopy

AI-Based Homology Modelling of Fatty Acid Transport Protein 1 Using AlphaFold: Structural Elucidation and Molecular Dynamics Exploration Fatty acid transport protein 1 (FATP1) is an integral transmembrane protein that is involved in facilitating the translocation of long-chain fatty acids (LCFA) across the plasma membrane, thereby orchestrating the importation of LCFA into the cell. FATP1 also functions as an acyl-CoA ligase, catalyzing the ATP-dependent formation of fatty acyl-CoA using LCFA and VLCFA (very-long-chain fatty acids) as substrates. It is expressed in various types of tissues and is involved in the regulation of crucial signalling pathways, thus playing a vital role in numerous physiological and pathological conditions. Structural insight about FATP1 is, thus, extremely important for understanding the mechanism of action of this protein and developing efficient treatments against its anomalous expression and dysregulation, which are often associated with pathological conditions such as breast cancer. As of now, there has been no prior prediction or evaluation of the 3D configuration of the human FATP1 protein, hindering a comprehensive understanding of the distinct functional roles of its individual domains. In our pursuit to unravel the structure of the most commonly expressed isoforms of FATP1, we employed the cutting-edge ALPHAFOLD 2 model for an initial prediction of the entire protein's structure. This prediction was complemented by molecular dynamics simulations, focusing on the most promising model. We predicted the structure of FATP1 in silico and thoroughly refined and validated it using coarse and molecular dynamics in the absence of the complete crystal structure. Their relative dynamics revealed the different properties of the characteristic FATP1. In silico ; Biology ; Transmembrane protein ; Homology modeling ; Molecular dynamics ; Transport protein ; Biochemistry ; Protein structure ; Fatty acid ; Gene isoform ; Cell biology ; Computational biology ; Biophysics ; Chemistry ; Gene ; Enzyme ; Receptor Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Metabolomics and Mass Spectrometry Studies
Authors: Ika Oktavianawati | Mardi Santoso | Sri Fatmawati
Source: Scientific Reports (article, 2023-09-14)
Field: Chemistry — Organic Chemistry

Metabolite profiling of Borneo’s Gonystylus bancanus through comprehensive extraction from various polarity of solvents Gonystylus bancanus wood or ramin wood has been generally known as a source of agarwood (gaharu) bouya, a kind of agarwood inferior type, or under the exported trading name of aetoxylon oil. The massive exploitation of ramin wood is causing this plant's extinction and putting it on Appendix II CITES and IUCN Red List of Threatened Species. To date, no scientific publication concerns the chemical exploration of G. bancanus wood and preserving this germplasm through its metabolite profiling. Therefore, research focused on chemical components profiling of G. bancanus is promised. This research is aimed to explore metabolomics and analyze the influence of solvent polarities on the partitioning of metabolites in G. bancanus wood. A range of solvents in different polarities was applied to provide comprehensive extraction of metabolites in G. bancanus wood. Moreover, a hydrodistillation was also carried out to extract the volatile compounds despite the non-volatile ones. LCMS and GCMS analyses were performed to identify volatile and non-volatile components in the extracts and essential oil. Multivariate data analysis was processed using Principal Component Analysis (PCA) and agglomerative hierarchical clustering. 142 metabolites were identified by LCMS analysis, while 89 metabolites were identified by GCMS analysis. Terpenoids, flavonoids, phenyl propanoids, and saccharides are some major compound classes available from LCMS data. Oxygenated sesquiterpenes, especially 10-epi-γ-eudesmol, and β-eudesmol, are the major volatile components identified from GCMS analysis. PCA of LCMS analysis demonstrated that PC1 discriminated two clusters: essential oil, dichloromethane, and n-hexane extracts were in the positive quadrant, while methanol and ethyl acetate extracts were in the negative quadrant. Three-dimensional analysis of GCMS data revealed that n-hexane extract was in the superior quadrant, and its composition can be significantly distinguished from other extracts and essential oil. G. bancanus wood comprises valuable metabolites, i.e., terpenoids, which benefit the essential oil industry. Comprehensive extraction by performing solvents in different polarities on G. bancanus wood could allow exploration of fully extracted metabolites, supported by the exhibition of identified metabolites from LCMS and GCMS analysis. Metabolite profiling ; Gas chromatography–mass spectrometry ; Metabolite ; Chemistry ; Pentadecanoic acid ; Metabolomics ; Chromatography ; Mass spectrometry ; Organic chemistry ; Biochemistry ; Fatty acid Wood and Agarwood Research ; Biological Stains and Phytochemicals ; Cultural Heritage Materials Analysis
Authors: Clemente Lauretti | Christian Tamantini | Alessandro Zompanti | Sara Cimini | Laura De Gara | Marco Santonico | Loredana Zollo
Source: IEEE Sensors Journal (article, 2023-11-30)
Field: Chemistry — Analytical Chemistry

A Low-Cost Multispectral Device for In-Field Fruit Ripening Assessment When assessing fruit ripeness, multispectral sensors offer a cheaper alternative to high-resolution spectroscopy. Additionally, they provide a more robust solution to environmental factors, in contrast to multi-spectral cameras. However, the low spectral resolution of multispectral devices proposed in the literature may not be sufficient to discriminate with acceptable accuracy a high number of different ripeness classes with machine learning techniques. Moreover, meaningful features to be given as input to the classification models are generally hand-crafted on the basis of the fruit to be evaluated, making the devices not versatile across other fruit categories. This paper aims at overcoming these limitations, by introducing a multispectral device for in-situ fruit-ripening evaluation that is i) affordable for a wide range of end-users, ii) robust to environmental factors, iii) capable of automatically finding the most meaningful features depending on the fruit categories to evaluate and iv) capable of discriminating a high number of fruit ripeness classes with high accuracy. The device integrates a broadband LED and a VIS-NIR multispectral sensor to gather information about the fruit’ ripeness levels through predictive machine learning algorithms. The proposed device was trained on a dataset of 450 spectral measurements acquired from tomatoes at six different ripeness stages. The results demonstrated the high capability of the proposed approach to recognize the tomatoes’ ripeness levels (average accuracy of 93.72%). Multispectral image ; Ripening ; Field (mathematics) ; Computer science ; Remote sensing ; Environmental science ; Artificial intelligence ; Geology ; Chemistry ; Mathematics ; Food science Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Geofrey Prudence Baitu | Omsalma Alsadig Adam Gadalla | Yeşim Benal Öztekin
Source: Tekirdağ Ziraat Fakültesi Dergisi (article, 2023-01-02)
Field: Chemistry — Analytical Chemistry

Traditional Machine Learning-Based Classification of Cashew Kernels Using Colour Features Cashew is one of the major commercial commodities contributing to the national economy of Tanzania as foreign revenue. And yet still the processing of cashew is run locally using manual labour for a big part. If processed well under ideal conditions, cashews kernels are expected to be white in colour. But due to various factors like prolonged roasting in the steam chambers or over-drying, some cashew kernels tend to have a slight brown colour, and these are referred to as scorched cashews. Despite sharing the same characteristics with white cashew kernels, including nutritional quality, these cashew kernels are supposed to be graded differently. In many places around the world, particularly in Tanzania, the sorting and grading process of cashew kernels is performed by hand. In international trade, cashew grading is very important and this means more effective and consistent methods need to be applied in this stage of production in order to increase the quality of the products. The objective of this study was to evaluate the use of traditional Machine Learning techniques in the classification of cashew kernels as white or scorched by using colour features. In this experiment, various colour features were extracted from the images. The extracted features include the means (μ), standard deviations (σ), and skewness (γ) of the channels in RGB and HSV colour spaces. The relevant features for this classification problem were selected by applying the wrapper approach using the Boruta Library in Python, and the irrelevant ones were removed. 5 models are studied and their efficiencies analysed. The studied models are Logistic Regression, Decision Tree, Random Forest, Support Vector Machine and K-Nearest Neighbour. The Decision Tree model recorded the least accuracy of 98.4%. The maximum accuracy of 99.8% was obtained in the Random Forest model with 100 trees. Due to simplicity in application and high accuracy, the Random Forest is recommended as the best model from this study. Mathematics ; Support vector machine ; Artificial intelligence ; Machine learning ; Computer science ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Neurological Disease Mechanisms and Treatments
Authors: Xiaohe Wang | Jing Wu | Wenbo Zhang | Jiang-Hao Wang | Qing‐Long Yun | Yiqing Wang | Yi Sui
Source: Scientific Reports (article, 2023-10-25)
Field: Materials Science — Materials Chemistry

An analysis of impact load and fragmentation dimension to explore energy dissipation patterns in coal crushing This research delineates the energy dissipation characteristics in coal crushing under impact loads, leveraging the capabilities of Separated Hopkinson Pressure Bar experimental system. A meticulous examination of both burst-prone and non-burst-prone coal samples during destruction processes was undertaken to decipher the dynamic compression mechanical attributes from perspectives of energy and fragmentatio's fractal dimensions. Burst-prone coal showcases a more pronounced escalation in fragmentation work in comparison to non-burst-prone samples, thereby illustrating a perceptible strain-rate dependent effect correlating with enhanced strain rates. Additionally, it was observed that incident, reflected, and transmitted energy trajectories for both sample categories follow an approximately linear ascendancy, albeit exhibiting diverse magnitudes. Burst-prone coal manifests a more rapid and focused energy growth compared to its non-burst-prone counterpart. When subjected to impact loads, a notable trend was discerned where the fragmentation's fractional dimension escalated persistently with both the incident energy and the crushing work, portraying a prominent growth effect. The insights garnered from this study pave the way for distinguishing between impacted and unimpacted coal samples using energy perspectives and fragmentation's fractal dimensions. Dissipation ; Coal ; Fractal dimension ; Fragmentation (computing) ; Split-Hopkinson pressure bar ; Elastic energy ; Coal mining ; Fractal ; Mechanics ; Materials science ; Mining engineering ; Strain rate ; Geology ; Physics ; Composite material ; Mathematics ; Biology ; Engineering ; Ecology ; Thermodynamics High-Velocity Impact and Material Behavior ; Rock Mechanics and Modeling ; Structural Response to Dynamic Loads
Authors: Miguel Marañón | Juan Fernández‐Novales | Javier Tardáguila | Salvador Gutiérrez | María P. Diago
Source: Biosystems Engineering (article, 2023-04-14)
Field: Chemistry — Analytical Chemistry

NIR attribute selection for the development of vineyard water status predictive models Near-Infrared spectroscopy (NIR) returns full spectra in the region between 750 and 2500 nm. Although a full spectrum provides extremely informative data, sometimes this enormous amount of detail is redundant and does not bring any additional information. In this work, different attribute selection methods for the development of vineyard water status predictive models are presented. Spectra from grapevine leaves were collected on-the-go (from a moving vehicle) along nine dates during the 2015 season in a commercial vineyard using a NIR spectrometer (1200–2100 nm). Contemporarily, the stem water potential (Ψstem) was also measured in the monitored vines. A manual selection, based on Variable Importance in Projection scores (VIP scores) to choose the spectrum intervals including the most important wavelengths (interval selection), the locally most important wavelengths in the spectrum (peak selection), as well as the Interval Partial Least Squares (IPLS) were tested as attribute selection methods. The results obtained for the estimation of Ψstem using the whole spectrum (R2P = 0.84, RMSEP = 0.167 MPa) were comparable to those yielded by the three attribute selection methods: the interval selection method (R2P = 0.80, RMSEP = 0.186 MPa), the peak selection method (R2P = 0.77, RMSEP = 0.201 MPa) and the IPLS (R2P ∼ 0.62–0.79, RMSEP ∼ 0.186–0.252 MPa). The highest simplification was provided by two IPLS models with three wavelengths and bandwidths of 20 and 4 nm that yielded R2P∼0.78 and RMSEP∼ 0.190 MPa. These results corroborate the suitability of a highly reduced selection of NIR wavelengths for the prediction of grapevine water status, and its utility to develop simpler multispectral devices for vineyard water status estimation. Vineyard ; Selection (genetic algorithm) ; Partial least squares regression ; Statistics ; Interval (graph theory) ; Mathematics ; Wavelength ; Spectrometer ; Environmental science ; Computer science ; Horticulture ; Physics ; Artificial intelligence ; Optics ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Horticultural and Viticultural Research