jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Changlun Gao | Ting Tang | Weibin Wu | Fangren Zhang | Yuanqiang Luo | Wei-Hao Wu | Beihuo Yao | Jiehao Li
Source: Remote Sensing (article, 2023-10-18)
Field: Chemistry — Analytical Chemistry

Hyperspectral Prediction Model of Nitrogen Content in Citrus Leaves Based on the CEEMDAN–SR Algorithm Nitrogen content is one of the essential elements in citrus leaves (CL), and many studies have been conducted to determine the nutrient content in CL using hyperspectral technology. To address the key problem that the conventional spectral data-denoising algorithms directly discard high-frequency signals, resulting in missing effective signals, this study proposes a denoising preprocessing algorithm, complete ensemble empirical mode decomposition with adaptive noise joint sparse representation (CEEMDAN–SR), for CL hyperspectral data. For this purpose, 225 sets of fresh CL were collected at the Institute of Fruit Tree Research of the Guangdong Academy of Agricultural Sciences, to measure their elemental nitrogen content and the corresponding hyperspectral data. First, the spectral data were preprocessed using CEEMDAN–SR, Stein’s unbiased risk estimate and the linear expansion of thresholds (SURE–LET), sparse representation (SR), Savitzky–Golay (SG), and the first derivative (FD). Second, feature extraction was carried out using principal component analysis (PCA), uninformative variables elimination (UVE), and the competitive adaptive re-weighted sampling (CARS) algorithm. Finally, partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and Gaussian process regression (GPR) were used to construct a CL nitrogen prediction model. The results showed that most of the prediction models preprocessed using the CEEMDAN–SR algorithm had better accuracy and robustness. The prediction models based on CEEMDAN–SR preprocessing, PCA feature extraction, and GPR modeling had an R2 of 0.944, NRMSE of 0.057, and RPD of 4.219. The study showed that the CEEMDAN–SR algorithm can be effectively used to denoise CL hyperspectral data and reduce the loss of effective information. The prediction model using the CEEMDAN–SR+PCA+GPR algorithm could accurately obtain the nitrogen content of CL and provide a reference for the accurate fertilization of citrus trees. Hyperspectral imaging ; Principal component analysis ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Partial least squares regression ; Mathematics ; Support vector machine ; Algorithm ; Statistics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote-Sensing Image Classification
Authors: Caleb R. Whatley | Nuwan K. Wijewardane | Raju Bheemanahalli | K. Raja Reddy | Yuzhen Lu
Source: Scientific Reports (article, 2023-04-18)
Field: Chemistry — Analytical Chemistry

Effects of fine grinding on mid-infrared spectroscopic analysis of plant leaf nutrient content Fourier transform mid infrared (FT-MIR) spectroscopy combined with modeling techniques has been studied as a useful tool for multivariate chemical analysis in agricultural research. A drawback of this method is the sample preparation requirement, in which samples must be dried and fine ground for accurate model calibrations. For research involving large sample sets, this may dramatically increase the time and cost of analysis. This study investigates the effect of fine grinding on model performance using leaf tissue from a variety of crop species. Dried leaf samples (N = 300) from various environmental conditions were obtained with data on 11 nutrients measured using chemical methods. The samples were scanned with attenuated total reflectance (ATR) and diffuse reflectance (DRIFT) FT-MIR techniques. Scanning was repeated after fine grinding for 2, 5, and 10 min. The spectra were analyzed for the 11 nutrients using partial least squares regression with a 75%/25% split for calibration and validation and repeated for 50 iterations. All analytes except for boron, iron, and zinc were well-modeled (average R<sup>2</sup> > 0.7), with higher R<sup>2</sup> values on ATR spectra. The 5 min level of fine grinding was found to be most optimal considering overall model performance and sample preparation time. Partial least squares regression ; Grinding ; Calibration ; Attenuated total reflection ; Nutrient ; Diffuse reflectance infrared fourier transform ; Multivariate statistics ; Environmental science ; Sample preparation ; Analytical Chemistry (journal) ; Materials science ; Diffuse reflection ; Biological system ; Chemistry ; Infrared spectroscopy ; Mathematics ; Environmental chemistry ; Chromatography ; Optics ; Metallurgy ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote Sensing in Agriculture
Authors: Han Guo | Mingjia Huo | Ruiyi Zhang | Pengtao Xie
Source:  (preprint, 2023-05-26)
Field: Materials Science — Materials Chemistry

ProteinChat: Towards Achieving ChatGPT-Like Functionalities on Protein 3D Structures &lt;p&gt;The study of proteins is critical in various scientific disciplines, but understanding their complex structure-function relationships remains challenging. Recent advancements in large language models (LLMs) have demonstrated their ability to comprehend task-specific knowledge, suggesting the potential for specially trained ChatGPT-like systems to accelerate protein research. In this work, we introduce ProteinGPT, a prototype model aimed at learning and understanding protein 3D structures. ProteinGPT enables users to upload proteins, ask questions, and engage in interactive conversations to gain insights. The ProteinChat system consists of three main components: a composite encoder block, a projection layer, and an LLM. The protein undergoes encoding to form a protein embedding, which is then projected to conform with the LLM. The LLM combines user questions with the embedding to generate informative answers. To train ProteinChat, we curated the RCSB-PDB Protein Description Dataset, comprising 143,508 protein-description pairs from publicly available sources. By leveraging the capabilities of ProteinGPT, researchers can potentially expedite their investigations into protein functionalities and structures, benefiting areas such as drug development and therapeutics, food science and nutrition, and various aspects of our lives. This initial step lays the foundation for further exploration and utilization of the ChatGPT-like system in protein research. The code is available at \url{https://github.com/UCSD-AI4H/proteinchat} and the dataset can be downloaded from \url{https://drive.google.com/file/d/1AeJW5BY5C-d8mKJjAULTax6WA4hzWS0N/view?usp=share_link}.&lt;/p&gt; Upload ; Computer science ; Task (project management) ; Function (biology) ; Embedding ; Block (permutation group theory) ; Code (set theory) ; World Wide Web ; Data science ; Artificial intelligence ; Programming language ; Engineering ; Biology Machine Learning in Materials Science ; Software Engineering Research ; Machine Learning in Bioinformatics
Authors: Tiziana Amoriello | Roberto Ciorba | Gianfilippo Ruggiero | Monica Amoriello | Roberto Ciccoritti
Source: Sensors (article, 2023-12-28)
Field: Chemistry — Analytical Chemistry

A Performance Evaluation of Two Hyperspectral Imaging Systems for the Prediction of Strawberries’ Pomological Traits Pomological traits are the major factors determining the quality and price of fresh fruits. This research was aimed to investigate the feasibility of using two hyperspectral imaging (HSI) systems in the wavelength regions comprising visible to near infrared (VisNIR) (400-1000 nm) and short-wave infrared (SWIR) (935-1720 nm) for predicting four strawberry quality attributes (firmness-FF, total soluble solid content-TSS, titratable acidity-TA, and dry matter-DM). Prediction models were developed based on artificial neural networks (ANN). The entire strawberry VisNIR reflectance spectra resulted in accurate predictions of TSS (R<sup>2</sup> = 0.959), DM (R<sup>2</sup> = 0.947), and TA (R<sup>2</sup> = 0.877), whereas good prediction was observed for FF (R<sup>2</sup> = 0.808). As for models from the SWIR system, good correlations were found between each of the physicochemical indices and the spectral information (R<sup>2</sup> = 0.924 for DM; R<sup>2</sup> = 0.898 for TSS; R<sup>2</sup> = 0.953 for TA; R<sup>2</sup> = 0.820 for FF). Finally, data fusion demonstrated a higher ability to predict fruit internal quality (R<sup>2</sup> = 0.942 for DM; R<sup>2</sup> = 0. 981 for TSS; R<sup>2</sup> = 0.976 for TA; R<sup>2</sup> = 0.951 for FF). The results confirmed the potential of these two HSI systems as a rapid and nondestructive tool for evaluating fruit quality and enhancing the product's marketability. Hyperspectral imaging ; Titratable acid ; Reflectivity ; Horticulture ; Mathematics ; Chemistry ; Food science ; Artificial intelligence ; Computer science ; Biology ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Psidium guajava Extracts and Applications
Authors: Wudi Zhao | Zhilu Wu | Zhendong Yin | Dasen Li
Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Reducing Moisture Effects on Soil Organic Carbon Content Estimation in Vis-NIR Spectra With a Deep Learning Algorithm When estimating soil organic carbon (SOC) using visible and near-infrared (Vis-NIR) spectra measured in situ, the interference of soil moisture content (SMC) needs to be eliminated. The existing SMC removal methods are mainly based on spectral transformation, but they change the original form of the soil spectrum. In this paper, a new deep learning-based SMC influence removal network (MIRNet) is proposed to establish the relationship between the spectra of moist soil and that of dry soil. This method constructs a spectral extraction module (SEM) with two one-dimensional (1-D) ghost modules to extract soil spectral characteristics and a context extraction module (CEM) with a two-layer dilated convolutional neural network (DiCNN) to extract the context information of the spectra. Then these extracted features are combined to reconstruct the SMC influence with a two-layer deconvolution using residual learning (Res). Finally, a new loss function that combining spectral distance and spectral shape measurement (D-S loss) is proposed. The input of MIRNet is the moist soil spectra, and the output is the dry soil spectra. Black soil collected from Harbin and yellow-brown soil collected from Nanjing are selected as the research objects. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> reaches 0.703, 0.747, 0.907, 0.892, 0.866, 0.907, and 0.926, respectively, when using spectra processed by external parameter orthogonalization (EPO), orthogonal signal correction (OSC), support vector regression (SVR), convolutional neural network (CNN), deep neural network (DNN), denoising convolutional neural network (DnCNN), and MIRNet. Therefore, the proposed MIRNet achieves competitive results compared with these state-of-the-art methods. Context (archaeology) ; Water content ; Spectral line ; Soil carbon ; Soil science ; Algorithm ; Artificial intelligence ; Mathematics ; Computer science ; Biological system ; Environmental science ; Soil water ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Tue Bjerg Bennike
Source: Frontiers in Immunology (review, 2023-10-06)
Field: Chemistry — Spectroscopy

Advances in proteomics: characterization of the innate immune system after birth and during inflammation Proteomics is the characterization of the protein composition, the proteome, of a biological sample. It involves the large-scale identification and quantification of proteins, peptides, and post-translational modifications. This review focuses on recent developments in mass spectrometry-based proteomics and provides an overview of available methods for sample preparation to study the innate immune system. Recent advancements in the proteomics workflows, including sample preparation, have significantly improved the sensitivity and proteome coverage of biological samples including the technically difficult blood plasma. Proteomics is often applied in immunology and has been used to characterize the levels of innate immune system components after perturbations such as birth or during chronic inflammatory diseases like rheumatoid arthritis (RA) and inflammatory bowel disease (IBD). In cancers, the tumor microenvironment may generate chronic inflammation and release cytokines to the circulation. In these situations, the innate immune system undergoes profound and long-lasting changes, the large-scale characterization of which may increase our biological understanding and help identify components with translational potential for guiding diagnosis and treatment decisions. With the ongoing technical development, proteomics will likely continue to provide increasing insights into complex biological processes and their implications for health and disease. Integrating proteomics with other omics data and utilizing multi-omics approaches have been demonstrated to give additional valuable insights into biological systems. Proteomics ; Proteome ; Innate immune system ; Computational biology ; Immune system ; Systems biology ; Bioinformatics ; Immunology ; Biology ; Medicine Advanced Proteomics Techniques and Applications ; Immune cells in cancer ; Neutrophil, Myeloperoxidase and Oxidative Mechanisms
Authors: Cheng Chen | Jingrong Chen | Xinpei Lin | Jiafu Yang | Huimin Qu | Lisong Li | Duanyan Zhang | Wei Wang | Xiangyu Chang | Zhenkun Guo | Ping Cai | Guangxia Yu | Wenya Shao | Hong Hu | Siying Wu | Huangyuan Li | Julia Bornhorst | Michael Aschner | Fuli Zheng
Source: Toxicological Sciences (article, 2023-08-16)
Field: Materials Science — Materials Chemistry

Evaluation of neurotoxicity and the role of oxidative stress of cobalt nanoparticles, titanium dioxide nanoparticles, and multiwall carbon nanotubes in <i>Caenorhabditis elegans</i> The widespread use of nanomaterials in daily life has led to increased concern about their potential neurotoxicity. Therefore, it is particularly important to establish a simple and reproducible assessment system. Representative nanomaterials, including cobalt nanoparticles (CoNPs), titanium dioxide nanoparticles (TiO2-NPs), and multiwall carbon nanotubes (MWCNTs), were compared in terms of their neurotoxicity and underlying mechanisms. In 0, 25, 50, and 75 μg/ml of these nanomaterials, the survival, locomotion behaviors, acetylcholinesterase (AchE) activity, reactive oxygen species production, and glutathione-S transferase 4 (Gst-4) activation in wildtype and transgenic Caenorhabditis elegans (C. elegans) were evaluated. All nanomaterials induced an imbalance in oxidative stress, decreased the ratio of survival, impaired locomotion behaviors, as well as reduced the activity of AchE in C. elegans. Interestingly, CoNPs and MWCNTs activated Gst-4, but not TiO2-NPs. The reactive oxygen species scavenger, N-acetyl-l-cysteine, alleviated oxidative stress and Gst-4 upregulation upon exposure to CoNPs and MWCNTs, and rescued the locomotion behaviors. MWCNTs caused the most severe damage, followed by CoNPs and TiO2-NPs. Furthermore, oxidative stress and subsequent activation of Gst-4 were involved in nanomaterials-induced neurotoxicity. Our study provides a comprehensive comparison of the neurotoxicity and mechanisms of typical nanomaterials, which could serve as a model for hazard assessment of environmental pollutants using C. elegans as an experimental model system. Neurotoxicity ; Oxidative stress ; Reactive oxygen species ; Nanomaterials ; Chemistry ; Glutathione ; Nanotoxicology ; Biophysics ; Nanoparticle ; Materials science ; Biochemistry ; Nanotechnology ; Toxicity ; Biology ; Organic chemistry ; Enzyme Nanoparticles: synthesis and applications ; Genetics, Aging, and Longevity in Model Organisms ; Advanced Nanomaterials in Catalysis
Authors: Georg Wallmann | Andrew Leduc | Nikolai Slavov
Source: Journal of Proteome Research (article, 2023-09-11)
Field: Chemistry — Spectroscopy

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS. Computer science ; Data quality ; Mass spectrometry ; Duty cycle ; Data acquisition ; Data mining ; Throughput ; Multiplexing ; Proteomics ; Metric (unit) ; Chemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Kenki Murakami | Wakana Kubo
Source: Applied Physics Express (article, 2023-08-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimizing broadband metamaterial absorber using deep reinforcement learning Abstract Optimization of the geometry of broadband metamaterial absorbers is crucial for improving the performance of optoelectronic devices. However, a large number of geometric parameters should be considered to achieve broad absorption, which is time-consuming. Herein, we propose a rapid and simple method for optimizing metamaterial absorbers dedicated to thermal radiation absorption using deep reinforcement learning. Deep reinforcement learning generated an ideal geometry for a broadband metamaterial absorber after 4 h, demonstrating the effectiveness of this technique for the rapid and effective optimization of metamaterial absorbers. Metamaterial ; Broadband ; Metamaterial absorber ; Materials science ; Absorption (acoustics) ; Reinforcement learning ; Computer science ; Optoelectronics ; Ideal (ethics) ; Optics ; Tunable metamaterials ; Telecommunications ; Artificial intelligence ; Physics ; Composite material Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Thermal Radiation and Cooling Technologies
Authors: Kai Yang | Sun Jian-qi | Hongning Liu | Weichao Yang | Lei Dong
Source: Polymers (article, 2023-05-21)
Field: Materials Science — Materials Chemistry

Study on the Thermogravimetric Kinetics of Dehydrated Sewage Sludge Regulated by Cationic Polyacrylamide and Sawdust With the continuous increase in sewage-sludge production worldwide, the pyrolytic disposal of sludge has received great attention. To build knowledge on the kinetics of pyrolysis, first, sludge was regulated using appropriate amounts of cationic polyacrylamide (CPAM) and sawdust to study their enhancing effect on dehydration. Due to the effects of the charge neutralization and skeleton hydrophobicity, a certain dose of CPAM and sawdust reduced the sludge's moisture content from 80.3% to 65.7%. Next, the pyrolysis characteristics of the dehydrated sludge regulated by CPAM and sawdust were investigated at a heating rate of 10~40 °C/min by using TGA method. The addition of sawdust enhanced the release of volatile substances and reduced the apparent activation energy of the sample. The maximum weight-loss rate decreased with the heating rate, and the DTG curves moved in the direction of high temperature. A model-free method, namely the Starink method, was adopted to calculate the apparent activation energies, which ranged from 135.3 kJ/mol to 174.8 kJ/mol. Combined with the master-plots method, the most appropriate mechanism function ultimately obtained was the nucleation-and-growth model. Sawdust ; Polyacrylamide ; Cationic polymerization ; Sewage sludge ; Pyrolysis ; Activation energy ; Dehydration ; Kinetics ; Chemistry ; Chemical engineering ; Pulp and paper industry ; Moisture ; Chromatography ; Materials science ; Nuclear chemistry ; Sewage ; Organic chemistry ; Polymer chemistry ; Environmental engineering ; Environmental science ; Biochemistry Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Coal Combustion and Slurry Processing
Authors: Chao Zhou | Denghao Li | Lei Lei | Gongxun Bai | Huanping Wang | Xianghua Zhang | Shiqing Xu | Jianrong Qiu
Source: Ceramics International (article, 2023-07-04)
Field: Materials Science — Materials Chemistry

Thermo-enhanced afterglow emission in Tm3+ doped fluoride nanoparticles for anti-counterfeiting application after X-ray excitation Afterglow ; Luminescence ; Materials science ; Photon upconversion ; Nanoparticle ; Excitation ; Persistent luminescence ; Doping ; Optoelectronics ; Excited state ; Nanotechnology ; Atomic physics ; Thermoluminescence ; Physics Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Radiation Detection and Scintillator Technologies
Authors: Iva Sučec | Nadia El Mammeri | Aurelio J. Dregni | Mei Hong
Source: The Journal of Physical Chemistry B (article, 2023-08-22)
Field: Chemistry — Spectroscopy

Rapid Determination of the Topology of Oligomeric α-Helical Membrane Proteins by Water- and Lipid-Edited Methyl NMR Single-span oligomeric α-helical transmembrane proteins are common in virus ion channels, which are targets of antiviral drugs. Knowledge about the high-resolution structures of these oligomeric α-helical bundles is so far scarce. Structure determination of these membrane proteins by solid-state NMR traditionally requires resolving and assigning protein chemical shifts and measuring many interhelical distances, which are time-consuming. To accelerate experimental structure determination, here we introduce a simple solid-state NMR approach that uses magnetization transfer from water and lipid protons to the protein. By detecting the water- and lipid-transferred intensities of the high-sensitivity methyl <sup>13</sup>C signals of Leu, Val, and Ile residues, which are highly enriched in these membrane proteins, we can derive models of the topology of these homo-oligomeric helical bundles. The topology is specified by the positions of amino acid residues in heptad repeats and the orientations of residues relative to the channel pore, lipids, and the helical interface. We demonstrate this water- and lipid-edited methyl NMR approach on the envelope (E) protein of SARS-CoV-2, the causative agent of the COVID-19 pandemic. We show that water-edited and lipid-edited 2D <sup>13</sup>C-<sup>13</sup>C correlation spectra can be measured with sufficient sensitivity. Even without resolving multiple residues of the same type in the NMR spectra, we can obtain the helical bundle topology. We apply these experiments to the structurally unknown E proteins of the MERS coronavirus and the human coronavirus NL63. The resulting structural topologies show interesting differences in the positions of the aromatic residues in these three E proteins, suggesting that these viroporins may have different mechanisms of activation and ion conduction. Topology (electrical circuits) ; Chemistry ; Membrane topology ; Chemical shift ; Membrane protein ; Lipid bilayer ; Transmembrane protein ; Protein structure ; Crystallography ; Nuclear magnetic resonance spectroscopy ; Protein secondary structure ; Membrane ; Stereochemistry ; Biochemistry Advanced NMR Techniques and Applications ; Lipid Membrane Structure and Behavior ; Protein Structure and Dynamics