jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tao Lu | Zihua Wang | Zhongyi Zhao | Zhike Zhao
Source: Agronomy (article, 2024-11-01)
Field: Chemistry — Analytical Chemistry

Recognition of Heat-Damaged Corn Seeds Based on Fusion of Laser Ultrasonic Signal and Infrared Image Features Corn is widely cultivated on a global scale. However, high temperatures during storage and transportation can lead to thermal damage to the kernels, negatively impacting their quality. Traditional methods for identifying heat-damaged grains primarily rely on manual inspection, which is characterized by low efficiency and accuracy. This study proposes a novel identification method that integrates laser ultrasonic signals with infrared image texture features. A pulsed laser stimulates the seeds to generate laser ultrasonic signals, while an infrared camera captures infrared images of the seeds. We extract time-domain, frequency-domain, and Hilbert-domain features from the laser ultrasonic signals, in addition to texture features from the infrared images. These features are combined using Canonical Correlation Analysis (CCA). Subsequently, the fused features are classified using a Backpropagation (BP) neural network, Support Vector Machine (SVM), and Particle Swarm Optimization–Support Vector Machine (PSO–SVM). The results indicate that the recognition rate achieved with the fused ‘signal-image’ features reaches 99.17%, providing a novel approach for detecting heat-damaged corn seeds. Ultrasonic sensor ; SIGNAL (programming language) ; Infrared ; Fusion ; Laser ; Artificial intelligence ; Acoustics ; Materials science ; Computer vision ; Optics ; Computer science ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Haoyun Su | Lok Hin Law | Yang Liu | Jianpan Huang | Kannie W. Y. Chan
Source: NMR in Biomedicine (article, 2024-08-12)
Field: Materials Science — Materials Chemistry

CEST effect of dimethyl sulfoxide at negative offset frequency Dimethyl sulfoxide (DMSO) has wide biomedical applications such as cryoprotectant and hydrophobic drug carrier. Here, we report for the first time that DMSO can generate a distinctive chemical exchange saturation transfer (CEST) signal at around -2 ppm. Structural analogs of DMSO, including aprotic and protic solvents, also demonstrated CEST signals from -1.4 to -3.8 ppm. When CEST detectable barbituric acid (BA) was dissolved in DMSO solution and was co-loaded to liposome, two obvious peaks at 5 and -2 ppm were observed, indicating that DMSO and related solvent system can be monitored in a label-free manner via CEST, which can be further applied to imaging drug nanocarriers. With reference to previous studies, there could be molecular interactions or magnetization transfer pathways, such as the relayed nuclear Overhauser enhancement (rNOE), that lead to this detectable CEST contrast at negative offset frequencies of the Z-spectrum. Our findings suggest that small molecules of organic solvents could be involved in magnetization transfer processes with water and readily detected by CEST magnetic resonance imaging (MRI), providing a new avenue for detecting solvent-water and solvent-drug interactions. Dimethyl sulfoxide ; Nuclear magnetic resonance ; Offset (computer science) ; Chemistry ; Physics ; Organic chemistry ; Computer science Lanthanide and Transition Metal Complexes ; Analytical Chemistry and Sensors ; Electron Spin Resonance Studies
Authors: Sri Vardhan Perla | Megha Vamsi Kiran Choda | Yuva Teja Anirudh Yelchuru | Brahmender Shaik | Prasanth Yalla
Source: Revue d intelligence artificielle (article, 2024-02-29)
Field: Chemistry — Analytical Chemistry

A Critical Implementation Strategy Proposed for Continuous Monitoring of Minerals in the Soil and to Identify the Diseases of Banana Plant Using Support Vector Machine Agriculture is a strong foundation of economy for many agriculture-based countries like India.In the economies of many nations, including India, where banana farming is quite important, agriculture is a key component.Musa Acuminata is the scientific name for the banana, and a banana tree normally lives for about 25 years.Banana cultivation typically lasts 9 to 12 months.The persistent threat of illnesses, which can drastically affect yield and quality, is just one of the many difficulties faced by the banana industry.This study focuses on overcoming these obstacles by observing the health of the soil and detecting infections in banana plants.The yield and crop quality of bananas can be negatively impacted by a number of variables, including illnesses.Developing an efficient method for spotting diseases in banana plants is our main goal, and we're also constantly keeping an eye on the condition of the soil.The main methodology we use for identifying diseases is SVM.In addition, we use procedures for soil monitoring to measure soil pH levels, minerals, and other essential elements that affect the health of the soil.The study shows good results in identifying diseases, ensuring prompt treatments to reduce risks associated with diseases.Furthermore, strong plant growth and increased yields are facilitated by realtime soil health monitoring.By tackling the ongoing threat of illnesses in banana cultivation through SVM-based disease identification and ongoing soil health monitoring, this research offers a vital contribution to the agricultural sector.The study shows promise in identifying diseases and guaranteeing prompt actions to reduce risks associated with diseases.Furthermore, strong plant growth and increased yields are facilitated by real-time soil health monitoring.This paper is focused on both types of diseases of banana plant and minerals in the soil. Support vector machine ; Agricultural engineering ; Computer science ; Process engineering ; Engineering ; Environmental science ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Diego Herrera‐Ochoa | Iván Llano | Consuelo Ripoll | Pierre Cybulski | Martin Kreuzer | Susana Rocha | Eva M. García‐Frutos | Iván Bravo | Andrés Garzón‐Ruiz
Source: ChemRxiv (preprint, 2024-02-19)
Field: Materials Science — Materials Chemistry

A Fluorescent Probe for Protein Misfolding and Aggregation Due to Oxidative Stress Based on a 7-Azaindole-BODIPY Derivative The development of new fluorescent probes as molecular sensors is a critical step for the understanding of molecular mechanisms. Probes based on BODIPY offer remarkable versatility in molecular sensing due to their unique properties. BODIPY-based probes exhibit high fluorescence quantum yields, exceptional photostability, and tunable absorption/emission wavelengths. Here, we report the synthesis and evaluation of a novel 7-azaindole-BODIPY derivative to probe hydrophobic proteins as well as protein misfolding and aggregation. In organic solvents, this compound exhibits two emissive excited states efficiently interconverting. In contrast, within aqueous environments, the formation of molecular aggregates induces distinct photophysical properties. The complex photophysics of this 7-azaindole-BODIPY derivate were used as a starting point to explore its sensing applications. In presence of albumin, the monomeric form of the probe is stabilized in the hydrophobic regions of the protein, leading to a significant increase of both the fluorescence emission intensity and lifetime. A similar effect was observed when the probe interacts with protein aggregates. Notably, the fluorescence emission is less sensitive to the presence of other macromolecules such as pepsin, DNA, Ficoll 40, and coconut oil. Fluorescence lifetime imaging microscopy (FLIM) and two-photon fluorescence microscopy performed on breast cancer cells (MCF-7) and lung cancer cells (A549) incubated with this probe revealed longer fluorescence lifetimes and higher emission intensity upon oxidative stress. It is known that cellular stress leads to the accumulation and aggregation of misfolded proteins. Protein misfolding in MCF-7 cells under oxidative stress conditions was confirmed by synchrotron FTIR microspectroscopy. These results show that protein misfolding and aggregation triggered by oxidative stress can be monitored using the probe here developed. BODIPY ; Oxidative stress ; Fluorescence ; Chemistry ; Derivative (finance) ; Protein aggregation ; Aggregation-induced emission ; Biophysics ; Oxidative phosphorylation ; Photochemistry ; Cell biology ; Biochemistry ; Biology ; Business ; Physics Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Click Chemistry and Applications
Authors: Aldo Rafael Martínez-Sifuentes | Ramón Trucíos-Caciano | Nuria Aide López-Hernández | Enrique Miguel-Valle | Juan Estrada Ávalos
Source: Nitrogen (article, 2024-05-29)
Field: Chemistry — Analytical Chemistry

Spectral Index-Based Estimation of Total Nitrogen in Forage Maize: A Comparative Analysis of Machine Learning Algorithms Nitrogen plays a fundamental role as a nutrient for the growth of leaves and the process of photosynthesis, as it directly influences the quality and yield of corn. The importance of knowing the foliar nitrogen content through Machine Learning algorithms will help determine the efficient use of nitrogen fertilization in a context of sustainable agronomic management by avoiding Nitrogen loss and preventing it from becoming a pollutant for the soil and the atmosphere. The combination of machine learning algorithms with vegetation spectral indices is a new practice that helps estimate parameters of agricultural importance such as nitrogen. The objective of the present study was to compare random forest and neural network algorithms for estimating total plant nitrogen with spectral indices. Five spectral indices were obtained from remotely piloted aircraft systems and analyzed by mean, maximum and minimum from each sample plot to finally obtain 15 indices, and total nitrogen was estimated from the georeferenced points. The most important variables were selected with backward, forward and stepwise methods and total nitrogen estimates by laboratory were compared with random forest models and artificial neural networks. The most important indices were NDREmax and TCARImax. Using 15 spectral indices, total nitrogen with a variance of 79% and 81% with random forest and artificial neural network, respectively, was estimated. And only using NDREmax and TCARmax indices, 73% and 79% were explained by random forest and artificial neural network, respectively. It is concluded that it is possible to estimate nitrogen in forage maize with two indices and it is recommended to analyze by phenological stage and with a greater number of field data. Forage ; Index (typography) ; Algorithm ; Nitrogen ; Estimation ; Agronomy ; Computer science ; Mathematics ; Agricultural engineering ; Artificial intelligence ; Machine learning ; Engineering ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Annabel R. Basford | Aaron Hero Bernardino | Paula C. P. Teeuwen | Benjamin D. Egleston | Joshua Humphreys | Kim E. Jelfs | Jonathan R. Nitschke | Imogen A. Riddell | Rebecca L. Greenaway
Source: ChemRxiv (preprint, 2024-12-12)
Field: Materials Science — Materials Chemistry

Development of an Automated Workflow for Screening the Assembly and Host-Guest Behaviour of Metal-Organic Cages towards Accelerated Discovery Metal-organic cages (MOCs) are a class of self-assembled materials with promising applications in chemical purifications, sensing, and catalysis. Their potential is, however, hampered by challenges in the targeted design of MOCs with desirable properties. MOC discovery is thus often reliant on trial-and-error approaches and brute-force manual screening, which are time-consuming, costly and material-intensive. Translating the synthesis and property screening of MOCs to an automated workflow is therefore attractive, to both accelerate discovery and provide the datasets crucial for data-led approaches to accelerate MOC discovery and to realize their targeted properties for specific applications. Here, an automated workflow for the streamlined assembly and property screening of MOCs was developed, incorporating automated high-throughput screening of variables pertinent to MOC synthesis, data curation and automated analysis, and development of a host:guest assay to rapidly assess binding behavior. Computational modelling supplemented this automated experimental workflow for post priori rationalization of experimental outcomes. This study lays the groundwork for future large-scale MOC screening: from a relatively modest screen of 24 precursor combinations under one set of reaction conditions, 3 clean MOC species were identified, and subsequent screening of their host:guest behavior highlighted trends in binding and the identification of potential applications in molecular separations. Workflow ; Computer science ; Rationalization (economics) ; Identification (biology) ; Host (biology) ; Nanotechnology ; Biochemical engineering ; Engineering ; Database ; Materials science ; Biology Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Dendrimers and Hyperbranched Polymers
Authors: Jixin Chen
Source: ChemRxiv (preprint, 2024-03-13)
Field: Chemistry — Spectroscopy

PyJCFit: A Non-Linear Regression Random Search Algorithm for Chemistry Data Fitting Hyperparameter optimization using non-linear least-square regression is a common fitting method for data analysis in chemistry, physics, biology, and engineering. It is sometimes challenging to balance various factors such as accuracy, computational time cost, and time cost in setting up the optimization. In this report, I introduce a naïve dynamic grid-searching algorithm named Python jump-chain fitting (PyJCFit) to reduce setting up time costs for a beginner in early-stage optimizations, which combines two different methods, grid and stochastic searching algorithms. The idea is to search all parameters heuristically and sequentially (a vector, not a grid matrix) in trusted bounds with an exponential distribution of space paying attention to the neighbor area of the guessed value. The distribution is somewhat random, and the chain does not require differential equations to optimize. PyJCFit is relatively slow but carries significant advantages in compound equations with breaks, peak searching, and global fitting; and various scoring functions beyond square residual, even those poorly behaved are allowed. Linear regression ; Algorithm ; Regression ; Computer science ; Mathematics ; Statistics Analytical Chemistry and Chromatography
Authors: Naiema Firdous Qadri | Ravinder Pal Singh | Jasmeen Gill
Source: International Journal for Research in Applied Science and Engineering Technology (review, 2024-08-04)
Field: Chemistry — Analytical Chemistry

Grading and Sorting of Capsicum Using Convolutional Neural Networks (CNN): A Review Abstract: Grading and sorting of capsicum, or bell peppers, is a critical task in the agricultural industry to ensure consistent quality control and efficient distribution. In recent years, Grading &amp; Sorting of Capsicum using Convolutional Neural Networks (CNNs), have shown promise in automating this process. The objective is to automate the Grading and Sorting process, which currently done manually and time-consuming. The proposed approach involves the collection of capturing dataset of capsicum images, extracting relevant features, and training a CNN model to classify the capsicum based on their quality criteria such as size, shape, color, and quality levels. These images are pre-processed to enhance their quality and standardize the inputs. A CNN architecture of 85 trained datasets and 33 tested datasets images using for accurate grading and sorting. After the training phase, the model's accuracy is evaluated using appropriate metrics such as precision, recall, or F1-score. The performance of the CNN model of grading and sorting system is determined based on the accuracy values, providing an objective measure of its effectiveness. Experimental results of testing datasets obtained an accuracy value of above 90% indicates that the system correctly classified, the capsicum into their respective grades or sorted categories 97.43% accuracy achieved Convolutional neural network ; Grading (engineering) ; Computer science ; Artificial intelligence ; Sorting ; Pattern recognition (psychology) ; Algorithm ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Ji Won Choi | Soo Been Cho | Woon-Ha Hwang | Byoung–Kwan Cho | Dae-Bin Song | Geonwoo Kim
Source: Korean Journal of Agricultural Science (article, 2024-12-01)
Field: Chemistry — Analytical Chemistry

Water stress level classification of sweet potato using infrared thermal imaging and plant growth indicators Recently, sudden droughts and floods due to the average temperature increase have become frequent and have become an emerging issue worldwide.It has caused serious decline in agricultural yield and product quality.Also, even though sweet potato is well known for growing under harsh environment, its yield can be affected by abnormal climates.Therefore, the importance of water stress monitoring of sweet potatoes has drawn substantial attention.As a result, a lot of research is being conducted.Then, various water stress level monitoring techniques have been recently developed.Therefore, in the current study, a nondestructive evaluation technique has been suggested for the water stress level evaluation of field-grown sweet potatoes.To accomplish this, thermal imagery and support vector machine (SVM) technique was used and a classifier for water stress evaluation (CSE) was developed.In addition, crop water stress index (CWSI) was derived from acquired thermal imagery and plat growth indices was used for developing the CSE.Consequently, the accuracy of the newly developed CSE was about 0.86.This study has demonstrated that CSE can be used to quantify water level stress and control the amount of irrigation water.Furthermore, based on the developed technique, the water stress level of different field-grown crops will be promising. Water stress ; Plant growth ; Thermal infrared ; Environmental science ; Horticulture ; Remote sensing ; Infrared ; Geography ; Biology ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Mohammad Khalid | Abubakar Abubakar | Mohd Faizan | L. Lokesh Kumar
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2024-12-29)
Field: Chemistry — Spectroscopy

Role of Chromatography in Pharmaceutical Analysis: Trends and Future Perspectives Abstract: Chromatography is a widely used analytical technique for separating and analyzing compounds present in complex mixtures. It operates on the principle of differential distribution of components between a stationary phase and a mobile phase, enabling the separation of substances based on their varying affinities for these phases. Over the years, chromatography has evolved into a versatile and indispensable tool in various fields, including chemistry, biochemistry, pharmacology, food science, environmental analysis, and clinical diagnostics. The primary types of chromatography include gas chromatography (GC), liquid chromatography (LC), ion-exchange chromatography (IEC), affinity chromatography, and size exclusion chromatography (SEC). Each type is suited to different applications, based on factors like the nature of the analytes, separation efficiency, and detection sensitivity. High-performance liquid chromatography (HPLC) and ultra-high-performance liquid chromatography (UHPLC) are particularly renowned for their resolution and speed, making them indispensable in pharmaceutical and clinical settings. This review provides an overview of the principles, types, applications, and recent advancements in chromatography. It highlights the integration of chromatography with other techniques, such as mass spectrometry (MS) and spectroscopy, to enhance its sensitivity and precision. Additionally, it discusses emerging trends, such as miniaturization and automation that are shaping the future of chromatographic analysis in research, quality control, and diagnostics. Chromatography ; Supercritical fluid chromatography ; Chemistry ; Gas chromatography ; High-performance liquid chromatography ; Analyte ; Hydrophilic interaction chromatography ; Ion chromatography ; Chromatography column ; Mass spectrometry Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Protein purification and stability
Authors: D. K. Vohra | DrDarpan Agrwal | Dr Shrey Chaudhari
Source: International Journal of Pharmaceutical Research and Applications (article, 2024-06-01)
Field: Materials Science — Biomaterials

The Impact of Nanotechnology on Drug Delivery: Current Trends and Future Perspectives Nanotechnology has emerged as a pivotal innovation in drug delivery, enhancing the precision and effectiveness of treatments. By utilizing nanoscale materials, drug solubility, bioavailability, and targeted delivery have significantly improved, especially in cancer therapies. Key advancements include the use of nanoparticles, such as liposomes and dendrimers, which allow for controlled drug release and minimized systemic toxicity. Despite these breakthroughs, challenges like biocompatibility, potential toxicity, and complex regulatory pathways remain barriers to widespread clinical use. Current trends focus on smart nanocarriers that respond to biological stimuli, enabling more tailored and realtime drug release. The future of this field may involve incorporating artificial intelligence to design more effective nanoparticles, potentially expanding applications beyond oncology to address rare and complex diseases. However, understanding the long-term effects of these technologies and addressing public safety and regulatory concerns is essential. To move forward, interdisciplinary collaboration is required between chemists, engineers, and medical professionals to translate lab research into clinical applications. Ethical considerations and public perception also play a role in gaining acceptance for nanomedicine. Future research should prioritize optimizing nanocarrier design, using biodegradable materials, and establishing clear regulatory guidelines. Incorporating AI could further advance personalized medicine, revolutionizing how treatments are administered. While the progress made in nanotechnology-driven drug delivery is promising, continued efforts are needed to fully unlock its potential and improve healthcare outcomes. Current (fluid) ; Drug delivery ; Nanotechnology ; Risk analysis (engineering) ; Business ; Engineering ; Materials science Nanoparticle-Based Drug Delivery