jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yaoyao Fan | Ting An | Qingyan Wang | Guang Yang | Wenqian Huang | Zheli Wang | Chunjiang Zhao | Xi Tian
Source: Frontiers in Plant Science (article, 2023-08-29)
Field: Chemistry — Analytical Chemistry

Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network The viability of Zea mays seed plays a critical role in determining the yield of corn. Therefore, developing a fast and non-destructive method is essential for rapid and large-scale seed viability detection and is of great significance for agriculture, breeding, and germplasm preservation. In this study, hyperspectral imaging (HSI) technology was used to obtain images and spectral information of maize seeds with different aging stages. To reduce data input and improve model detection speed while obtaining more stable prediction results, successive projections algorithm (SPA) was used to extract key wavelengths that characterize seed viability, then key wavelength images of maize seed were divided into small blocks with 5 pixels ×5 pixels and fed into a multi-scale 3D convolutional neural network (3DCNN) for further optimizing the discrimination possibility of single-seed viability. The final discriminant result of single-seed viability was determined by comprehensively evaluating the result of all small blocks belonging to the same seed with the voting algorithm. The results showed that the multi-scale 3DCNN model achieved an accuracy of 90.67% for the discrimination of single-seed viability on the test set. Furthermore, an effort to reduce labor and avoid the misclassification caused by human subjective factors, a YOLOv7 model and a Mask R-CNN model were constructed respectively for germination judgment and bud length detection in this study, the result showed that mean average precision (mAP) of YOLOv7 model could reach 99.7%, and the determination coefficient of Mask R-CNN model was 0.98. Overall, this study provided a feasible solution for detecting maize seed viability using HSI technology and multi-scale 3DCNN, which was crucial for large-scale screening of viable seeds. This study provided theoretical support for improving planting quality and crop yield. Hyperspectral imaging ; Germplasm ; Convolutional neural network ; Pixel ; Scale (ratio) ; Computer science ; Germination ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Agronomy ; Biology ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Özcan Çataltaş | Kemal Tütüncü
Source: PeerJ Computer Science (article, 2023-03-09)
Field: Chemistry — Analytical Chemistry

Detection of protein, starch, oil, and moisture content of corn kernels using one-dimensional convolutional autoencoder and near-infrared spectroscopy R<sup>2</sup>, RMSE, and RMSPE were used to show the performance of the proposed model. The created one-dimensional convolutional autoencoder model achieved a high reconstruction rate with a mean RMSPE value of 1.90% and 2.27% for calibration and prediction sets, respectively. This way, a spectrum with 700 features was converted to only 32 features. The created MLR models which use these features as input were compared to partial least squares regression and principal component regression combined with various preprocessing methods. Experimental results indicate that the proposed method has superior performance, especially in MP5 and MP6 datasets. Autoencoder ; Principal component analysis ; Partial least squares regression ; Pattern recognition (psychology) ; Principal component regression ; Artificial intelligence ; Mathematics ; Near-infrared spectroscopy ; Mean squared error ; Linear regression ; Statistics ; Biological system ; Computer science ; Deep learning ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Alexander Hinderhofer | Alessandro Greco | Vladimir Starostin | Valentin Munteanu | Linus Pithan | Alexander Gerlach | Frank Schreiber
Source: Journal of Applied Crystallography (review, 2023-01-24)
Field: Materials Science — Materials Chemistry

Machine learning for scattering data: strategies, perspectives and applications to surface scattering Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X-ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community. Scattering ; Reflectometry ; Computer science ; Neutron reflectometry ; Surface (topology) ; Set (abstract data type) ; Artificial neural network ; Neutron scattering ; Optics ; Small-angle neutron scattering ; Machine learning ; Physics ; Mathematics X-ray Diffraction in Crystallography ; Geophysical and Geoelectrical Methods ; Nuclear Physics and Applications
Authors: Yue Zhou | Ping Sun | Yongbin Cao | Jiahao Yang | Qingzhi Wu | Jian Peng
Source: Journal of Nanobiotechnology (article, 2023-12-10)
Field: Chemistry — Organic Chemistry

Biocompatible copper formate-based nanoparticles with strong antibacterial properties for wound healing Copper-based antibacterial materials have emerged as a potential alternative for combating bacterial infections, which continue to pose significant health risks. Nevertheless, the use of copper-based nanoparticles as antibacterial agents has faced challenges due to their toxicity towards cells and tissues. To overcome this obstacle, we propose a new approach using a contact-active copper-based nanoparticles called polydopamine (PDA)-coated copper-amine (Cuf-TMB@PDA). The positively charged surface of Cuf-TMB@PDA enables efficient targeting of negatively charged bacteria, allowing controlled release of Cu(II) into the bacterial cell membrane. Moreover, Cuf-TMB@PDA exhibits similar ·OH signals as Cuf-TMB suspensions in previous work. In cytotoxicity assays conducted over 72 h, Cuf-TMB@PDA demonstrated an efficacy of 98.56%, while releasing lower levels of Cu(II) that were less harmful to cells, resulting in enhanced antimicrobial effects. These antimicrobial properties are attributed to the synergistic effects of charge-contact activity of PDA, controlled release of Cu(II), and free radicals. Subsequent in vivo experiments confirmed the strong antimicrobial potency of Cuf-TMB@PDA and its ability to promote wound healing. Antimicrobial ; Chemistry ; Copper ; Nanoparticle ; Cytotoxicity ; Antibacterial activity ; Bacteria ; Formate ; Combinatorial chemistry ; In vivo ; Bacterial growth ; Biocompatible material ; Wound healing ; Biophysics ; Nuclear chemistry ; Nanotechnology ; In vitro ; Biochemistry ; Materials science ; Organic chemistry ; Biomedical engineering ; Medicine ; Immunology ; Biology Antimicrobial agents and applications ; Wound Healing and Treatments ; Graphene and Nanomaterials Applications
Authors: Kshitiz Dhakal | Upasana Sivaramakrishnan | Xuemei Zhang | Kassaye Belay | Joseph Oakes | Xing Wei | Song Li
Source: Sensors (article, 2023-03-28)
Field: Chemistry — Analytical Chemistry

Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels Fusarium head blight (FHB) is a disease of small grains caused by the fungus <i>Fusarium graminearum</i>. In this study, we explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels. We evaluated the use of HSI for disease classification and correlated the damage with the mycotoxin deoxynivalenol (DON) content. Computational analyses were carried out to determine which machine learning methods had the best accuracy to classify different levels of damage in wheat kernel samples. The classes of samples were based on the DON content obtained from Gas Chromatography-Mass Spectrometry (GC-MS). We found that G-Boost, an ensemble method, showed the best performance with 97% accuracy in classifying wheat kernels into different severity levels. Mask R-CNN, an instance segmentation method, was used to segment the wheat kernels from HSI data. The regions of interest (ROIs) obtained from Mask R-CNN achieved a high mAP of 0.97. The results from Mask R-CNN, when combined with the classification method, were able to correlate HSI data with the DON concentration in small grains with an R<sup>2</sup> of 0.75. Our results show the potential of HSI to quantify DON in wheat kernels in commercial settings such as elevators or mills. Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Kernel (algebra) ; Mycotoxin ; Fusarium ; Segmentation ; Mathematics ; Computer science ; Horticulture ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Advanced Chemical Sensor Technologies
Authors: Yiran Wu | Xinhua Zhu | Qiangsheng Huang | Yuan Zhang | Julian Evans | Sailing He
Source: Applied Sciences (article, 2023-07-15)
Field: Chemistry — Analytical Chemistry

Predicting the Quality of Tangerines Using the GCNN-LSTM-AT Network Based on Vis–NIR Spectroscopy Fruit quality assessment plays a crucial role in determining their market value, consumer acceptance, and post-harvest management. In recent years, spectroscopic techniques have gained significant attention as non-destructive methods for evaluating fruit quality. In this study, we propose a novel deep-learning network, called GCNN-LSTM-AT, for the prediction of five important parameters of tangerines using visible and near-infrared spectroscopy (Vis–NIR). The quality attributes include soluble solid content (SSC), total acidity (TA), acid–sugar ratio (A/S), firmness, and Vitamin C (VC). The proposed model combines the strengths of graph convolutional network (GCN), convolutional neural networks (CNNs), and long short-term memory (LSTM) to capture both spatial and sequential dependencies in the spectra data, and incorporates an attention mechanism to enhance the discriminative ability of the model. To investigate the effectiveness and stability of the model, comparisons with three traditional machine-learning algorithms—moving window partial least squares (MWPLS), random forest (RF), and support vector regression (SVR)—and two deep neural networks—DeepSpectra2D and CNN-AT—are provided. The results have shown that the GCNN-LSTM-AT network outperforms other algorithms and models, achieving accurate predictions for SSC (R2: 0.9885, RMSECV: 0.1430 ∘Brix), TA (R2: 0.8075, RMSECV: 0.0868%), A/S (R2: 0.9014, RMSECV: 1.9984), firmness (R2: 0.9472, RMSECV: 0.0294 kg), and VC (R2: 0.7386, RMSECV: 29.4104 mg/100 g) of tangerines. Discriminative model ; Convolutional neural network ; Computer science ; Artificial intelligence ; ABX test ; Graph ; Support vector machine ; Deep learning ; Machine learning ; Pattern recognition (psychology) ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Smart Agriculture and AI
Authors: Hasan Ahmed | Md Ashikur Rahaman Noyon | Md. Elias Uddin | Mamun Jamal | Sathish Kumar Palaniappan
Source: Polymer Composites (article, 2023-08-09)
Field: Materials Science — Polymers and Plastics

Biodegradable and flexible fiber‐reinforced composite sheet from tannery solid wastes: An approach of waste minimization Abstract The rising concern towards environmental issues and non‐recyclable solid wastes preservation needs has led to the development of high‐performance biodegradable composite materials with new functionalities. In this regard, the current study focused on fabricating flexible and biodegradable composite sheets by utilizing waste buffing dust (BD) and sugarcane fiber (SF). Flexible composite sheets were fabricated using simple solution casting method with different mixing ratios of BD and SF. Wide range of characterizations like ultraviolet‐vis spectroscopy, Fourier‐transform infrared spectroscopy, thermogravimetric analysis, tensile tests, x‐ray diffractometry, scanning electron microscopy, oxygen gas transmittance rate, and soil burial tests were conducted to comprehensively evaluate their performance. The obtained results were highly promising which reveals strong bonding between BD and SF along with a uniform surface quality. Particularly, the enhancement in various key characteristics was observed for composite sheets compared to pure BD sheets. The composite sheets with the optimal content of natural rubber latex demonstrated a significant increase in tensile strength, elongation, hardness, and density by 61%, 42%, 37%, and 44%, respectively. Additionally, the composite sheet has witnessed significant improvements with an increase by 36% in gas barrier property and an enhancement of 39% in biodegradability. These findings highlight the potential of these composite sheets as a versatile material suitable for a wide range of applications which includes packaging, interior furnishing industries, and reinforcing elements in the footwear industry. Numerous advantages like simplicity, cost‐effectiveness, and flexibility of these composite sheets offer an environmentally friendly alternative that is capable of reducing waste and promoting sustainable practices. Highlights Fabricated composite sheets from waste BD and SF. Strong bonding and uniform surface quality observed. Improved thermal stability, gas barrier, and biodegradability. Enhancements in tensile strength, elongation, hardness, and density. Versatile eco‐friendly material for packaging and furnishings. Materials science ; Composite number ; Ultimate tensile strength ; Composite material ; Thermogravimetric analysis ; Fourier transform infrared spectroscopy ; Fiber ; Biodegradation ; Chemical engineering Natural Fiber Reinforced Composites ; Electrospun Nanofibers in Biomedical Applications ; Advanced Cellulose Research Studies
Authors: Presha Rajbhandari | Taruna V. Neelakantan | Noreen Hosny | Brent R. Stockwell
Source: Trends in Pharmacological Sciences (review, 2023-12-15)
Field: Chemistry — Spectroscopy

Spatial pharmacology using mass spectrometry imaging Mass spectrometry imaging ; Computational biology ; Drug discovery ; Drug development ; Systems pharmacology ; Subtyping ; ADME ; Computer science ; Data science ; Drug ; Pharmacology ; Bioinformatics ; Mass spectrometry ; Chemistry ; Medicine ; Biology Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: João Meneses | Michael González‐Durruthy | Eli Fernández-de-Gortari | Alla P. Toropova | Andrey A. Toropov | Ernesto Alfaro‐Moreno
Source: Particle and Fibre Toxicology (article, 2023-05-22)
Field: Materials Science — Materials Chemistry

A Nano-QSTR model to predict nano-cytotoxicity: an approach using human lung cells data The proposed model suggests that a decrease in the ENMs diameter could significantly increase their potential ability to access lung subcellular compartments (e.g., mitochondria and nuclei), promoting strong nano-cytotoxicity and epithelial barrier dysfunction. Additionally, the presence of polyethylene glycol (PEG) as a surface coating could prevent the potential release of cytotoxic metal ions, promoting lung cytoprotection. Overall, the current work could pave the way for efficient decision-making, prediction, and mitigation of the potential occupational and environmental ENMs risks. Cytotoxicity ; Nano- ; Human lung ; Nanotechnology ; Decision tree ; Quantitative structure–activity relationship ; Chemistry ; Biological system ; Computer science ; Materials science ; Biology ; Artificial intelligence ; Machine learning ; Biochemistry ; In vitro ; Cell culture ; Genetics Nanoparticles: synthesis and applications ; Computational Drug Discovery Methods
Authors: Zhixi Li | Fanqianhui Yu
Source: Foods (review, 2023-08-20)
Field: Materials Science — Biomaterials

Recent Advances in Lycopene for Food Preservation and Shelf-Life Extension In recent years, there has been increasing concern about the safety of additives used to extend the shelf-life of food products. As a result, lycopene, a natural phytochemical compound, has attracted attention, as it has been demonstrated to be a potential alternative to traditional artificial antioxidants, with significant health benefits when applied to food preservation. Based on this, this review introduces the specific forms of lycopene currently used as an antioxidant in foods, both in its naturally occurring forms in fruits and vegetables and in artificially added forms involving technologies such as composite coating, active film packaging, emulsion, and microcapsules. In addition, it also provides a comprehensive summary of the effects and progress of lycopene in the preservation of different types of food products, such as meat, seafood, oil, dairy products, fruits, and vegetables, in the last decade. At last, it also points out the limitations of lycopene, including its insolubility in water, dark color, and high sensitivity to heat or light, as well as the potential solutions to load lycopene on suitable carriers, such as combining lycopene with antimicrobial substances or other actives, in order to broaden its applications as an antioxidant in future foods. Lycopene ; Health benefits ; Shelf life ; Food science ; Phytochemical ; Food preservation ; Emulsion ; Antioxidant ; Food products ; Biotechnology ; Chemistry ; Biochemical engineering ; Carotenoid ; Biology ; Engineering ; Medicine ; Traditional medicine ; Biochemistry Nanocomposite Films for Food Packaging ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Aldana R. Galiyeva | Arailym T. Daribay | Tolkyn Zhumagaliyeva | Lyazzat Zh. Zhaparova | D. T. Sadyrbekov | Yerkeblan M. Tazhbayev
Source: Polymers (article, 2023-06-22)
Field: Materials Science — Biomaterials

Human Serum Albumin Nanoparticles: Synthesis, Optimization and Immobilization with Antituberculosis Drugs The aim of this study was to create nanoparticles of human serum albumin immobilized with anti-TB drugs (rifampicin, isoniazid) using the desolvation method. Central Composite Design (CCD) was applied to study the effect of albumin, urea, L-cysteine, rifampicin and isoniazid concentration on particle size, polydispersity and loading degree of the drugs. The optimized nanoparticles were spherical in shape with an average particle size of 216.7 ± 3.7 nm and polydispersity of 0.286 ± 4.9. The loading degree of rifampicin and isoniazid in the optimized nanoparticles were 44% and 27%, respectively. The obtained nanoparticles were examined by Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC); the results showed the absence of drug-polymer interactions. The drug release from the polymer matrix was studied using dialysis membranes. Human serum albumin ; Albumin ; Nanoparticle ; Human albumin ; Serum albumin ; Pharmacology ; Medicine ; Chemistry ; Chromatography ; Materials science ; Biochemistry ; Nanotechnology Nanoparticle-Based Drug Delivery ; Protein Interaction Studies and Fluorescence Analysis ; Advanced Drug Delivery Systems
Authors: Stephanie A. Wankowicz | Ashraya Ravikumar | Shivani Sharma | Blake T. Riley | Akshay Raju | Daniel W. Hogan | Jessica Flowers | Henry van den Bedem | D.A. Keedy | James S. Fraser
Source: eLife (article, 2023-09-05)
Field: Materials Science — Materials Chemistry

Automated multiconformer model building for X-ray crystallography and cryo-EM In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R<sub>free</sub> and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function. Cryo-electron microscopy ; Structural biology ; Computer science ; Biomolecular structure ; Software ; Function (biology) ; Range (aeronautics) ; Protein structure ; Algorithm ; Statistical physics ; Physics ; Chemistry ; Biology ; Biophysics ; Materials science Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Hongkai Ning | Zhihao Yu | Taotao Li | Haoliang Shen | Gen Long | Yi Shi | Xinran Wang
Source: Science China Information Sciences (article, 2023-05-17)
Field: Materials Science — Materials Chemistry

From lab to fab: path forward for 2D material electronics Electronics ; Key (lock) ; Integrated circuit ; Computer science ; Node (physics) ; Nanotechnology ; Computation ; Engineering physics ; Electrical engineering ; Systems engineering ; Engineering ; Materials science 2D Materials and Applications ; MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices