jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dongyan Zhang | Daoyong Wang | Chunyan Gu | Jin Ning | Haitao Zhao | Chen Gao | Hongyi Liang | Liang Dong
Source: Remote Sensing (article, 2019-10-13)
Field: Chemistry — Analytical Chemistry

Using Neural Network to Identify the Severity of Wheat Fusarium Head Blight in the Field Environment Fusarium head blight (FHB), one of the most important diseases of wheat, mainly occurs in the ear. Given that the severity of the disease cannot be accurately identified, the cost of pesticide application increases every year, and the agricultural ecological environment is also polluted. In this study, a neural network (NN) method was proposed based on the red-green-blue (RGB) image to segment wheat ear and disease spot in the field environment, and then to determine the disease grade. Firstly, a segmentation dataset of single wheat ear was constructed to provide a benchmark for the segmentation of the wheat ear. Secondly, a segmentation model of single wheat ear based on the fully convolutional network (FCN) was established to effectively realize the segmentation of the wheat ear in the field environment. An FHB segmentation algorithm was proposed based on a pulse-coupled neural network (PCNN) with K-means clustering of the improved artificial bee colony (IABC) to segment the diseased spot of wheat ear by automatic optimization of PCNN parameters. Finally, the disease grade was calculated using the ratio of the disease spot to the whole wheat ear. The experimental results show that: (1) the accuracy of the segmentation model for single wheat ear constructed in this study is 0.981. The segmentation time is less than 1 s, indicating that the model can quickly and accurately segment wheat ear in the field environment; (2) the segmentation method of the disease spot performed under each evaluation indicator is improved compared with the traditional segmentation methods, and the accuracy is 0.925 in the disease severity identification. These research results can provide important reference value for grading wheat FHB in the field environment, which also can be beneficial for real-time monitoring of other crops’ diseases under near-Earth remote sensing. Segmentation ; Computer science ; Artificial intelligence ; Artificial neural network ; Pattern recognition (psychology) ; Image segmentation ; Blight ; Biology ; Agronomy Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Mycotoxins in Agriculture and Food
Authors: Zeling Chen | Ting Wu | Cheng Xiang | Xiaoyan Xu | Xingguo Tian
Source: Molecules (article, 2019-08-06)
Field: Chemistry — Analytical Chemistry

Rapid Identification of Rainbow Trout Adulteration in Atlantic Salmon by Raman Spectroscopy Combined with Machine Learning This study intends to evaluate the utilization potential of the combined Raman spectroscopy and machine learning approach to quickly identify the rainbow trout adulteration in Atlantic salmon. The adulterated samples contained various concentrations (0-100% <i>w</i>/<i>w</i> at 10% intervals) of rainbow trout mixed into Atlantic salmon. Spectral preprocessing methods, such as first derivative, second derivative, multiple scattering correction (MSC), and standard normal variate, were employed. Unsupervised algorithms, such as recursive feature elimination, genetic algorithm (GA), and simulated annealing, and supervised K-means clustering (KM) algorithm were used for selecting important spectral bands to reduce the spectral complexity and improve the model stability. Finally, the performances of various machine learning models, including linear regression, nonlinear regression, regression tree, and rule-based models, were verified and compared. The results denoted that the developed GA-KM-Cubist machine learning model achieved satisfactory results based on MSC preprocessing. The determination coefficient (R<sup>2</sup>) and root mean square error of prediction sets (RMSEP) in the test sets were 0.87 and 10.93, respectively. These results indicate that Raman spectroscopy can be used as an effective Atlantic salmon adulteration identification method; further, the developed model can be used for quantitatively analyzing the rainbow trout adulteration in Atlantic salmon. Rainbow trout ; Artificial intelligence ; Preprocessor ; Salmo ; Cluster analysis ; Biological system ; Machine learning ; Trout ; Computer science ; Pattern recognition (psychology) ; Mathematics ; Algorithm ; Fishery ; Fish <Actinopterygii> ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Insuck Baek | Moon S. Kim | Byoung–Kwan Cho | Changyeun Mo | Jinyoung Y. Barnaby | Anna M. McClung | Mirae Oh
Source: Applied Sciences (article, 2019-03-12)
Field: Chemistry — Analytical Chemistry

Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds The inspection of rice grain that may be infected by seedborne disease is important for ensuring uniform plant stands in production fields as well as preventing proliferation of some seedborne diseases. The goal of this study was to use a hyperspectral imaging (HSI) technique to find optimal wavelengths and develop a model for detecting discolored, diseased rice seed infected by bacterial panicle blight (Burkholderia glumae), a seedborne pathogen. For this purpose, the HSI data spanning the visible/near-infrared wavelength region between 400 and 1000 nm were collected for 500 sound and discolored rice seeds. For selecting optimal wavelengths to use for detecting diseased seed, a sequential forward selection (SFS) method combined with various spectral pretreatments was employed. To evaluate performance based on optimal wavelengths, support vector machine (SVM) and linear and quadratic discriminant analysis (LDA and QDA) models were developed for detection of discolored seeds. As a result, the violet and red regions of the visible spectrum were selected as key wavelengths reflecting the characteristics of the discolored rice seeds. When using only two or only three selected wavelengths, all of the classification methods achieved high classification accuracies over 90% for both the calibration and validation sample sets. The results of the study showed that only two to three wavelengths are needed to differentiate between discolored, diseased and sound rice, instead of using the entire HSI wavelength regions. This demonstrates the feasibility of developing a low cost multispectral imaging technology based on these selected wavelengths for non-destructive and high-throughput screening of diseased rice seed. Hyperspectral imaging ; Multispectral image ; Wavelength ; Selection (genetic algorithm) ; Remote sensing ; Pattern recognition (psychology) ; Mathematics ; Artificial intelligence ; Computer science ; Materials science ; Optoelectronics ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Essential Oils and Antimicrobial Activity
Authors: Yang Yu | Sergio A. Velastín | Fei Yin
Source: Information Processing in Agriculture (article, 2019-11-27)
Field: Chemistry — Analytical Chemistry

Automatic grading of apples based on multi-features and weighted K-means clustering algorithm In this paper, a fast and effective method based on multiple image features and a weighted K-means clustering algorithm is proposed to achieve the automatic grading of apples. The method provides a novel way of using four images (top, bottom and two sides) and average gray values for each apple to distinguish between the apple defects, stem and calyx. Furthermore, weighted features (MCSAD (maximum cross-sectional average diameter), circularity, PRA (proportion of red area) and defect regions) were carefully selected according to the requirements of the national apple grading standard, which improves the practicality of the proposed method. Finally, qualitative and quantitative evaluation results demonstrate that the total accuracy of the proposed multi-feature grading method is greater than 96%, which provides encouragement for the additional research and implementation of multi-feature automatic grading for the fruit industry. Grading (engineering) ; Cluster analysis ; Artificial intelligence ; Computer science ; Calyx ; k-means clustering ; Pattern recognition (psychology) ; Algorithm ; Mathematics ; Data mining ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Michele Genangeli | Ron M. A. Heeren | Tiffany Porta Siegel
Source: Analytical and Bioanalytical Chemistry (article, 2019-11-11)
Field: Chemistry — Spectroscopy

Tissue classification by rapid evaporative ionization mass spectrometry (REIMS): comparison between a diathermic knife and CO2 laser sampling on classification performance Abstract The increasing need for rapid, in situ, and robust tissue profiling approaches in the context of intraoperative diagnostics has led to the development of a large number of ambient ionization-based surface sampling strategies. This paper compares the performances of a diathermic knife and a CO 2 laser handpiece, both clinically approved, coupled to a rapid evaporative ionization mass spectrometry (REIMS) source for quasi-instantaneous tissue classification. Several fresh meat samples (muscle, liver, bone, bone marrow, cartilage, skin, fat) were obtained from different animals. Overall, the laser produced cleaner cuts and more reproducible and higher spectral quality signals when compared with the diathermic knife (CV laser = 9–12%, CV diathermic = 14–23%). The molecular profiles were subsequently entered into a database and PCA/LDA classification/prediction models were built to assess if the data generated with one sampling modality can be employed to classify the data generated with the other handpiece. We demonstrate that the correct classification rate of the models increases (+ 25%) with the introduction of a model based on peak lists that are tissue-specific and common to the two handpieces, compared with considering solely the whole molecular profile. This renders it possible to use a unique and universal database for quasi-instantaneous tissue recognition which would provide similar classification results independent of the handpiece used. Furthermore, the laser was able to generate aerosols rich in lipids from hard tissues such as bone, bone marrow, and cartilage. Combined, these results demonstrate that REIMS is a valuable and versatile tool for instantaneous identification/classification of hard tissue and coupling to different aerosol-generating handpieces expands its field of application. Mass spectrometry ; Sampling (signal processing) ; Analytical Chemistry (journal) ; Ionization ; Chemistry ; Laser ; Chromatography ; Environmental chemistry ; Optics ; Ion ; Physics ; Organic chemistry Mass Spectrometry Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies
Authors: Chengyun Hua | Lucas Lindsay | Xiangwen Chen | Austin J. Minnich
Source: Physical review. B./Physical review. B (article, 2019-08-21)
Field: Materials Science — Materials Chemistry

Generalized Fourier's law for nondiffusive thermal transport: Theory and experiment Fourier's law, which relates the heat flux and temperature fields at macroscopic length scales, is fundamental to the mathematical description of heat transport. While it is well-known that Fourier's law breaks down at lengths comparable to the mean free path of the heat carriers, an appropriate modification has remained unknown until now. In the present work, the authors derive analytically a generalized Fourier's law that is valid over transport regimes spanning ballistic to diffusive. This relation shows that in the nondiffusive regime the heat flux and temperature fields not only have a nonlocal relationship but also depend on the properties of the heat source itself, a prediction that is verified by thermal spectroscopy experiments. This constitutive law provides a theoretical basis to understand nonlocal effects in the transport of heat at mesoscopic length scales. Fourier transform ; Boltzmann equation ; Thermal conductivity ; Thermal conduction ; Phonon ; Physics ; Ballistic conduction ; Statistical physics ; Heat flux ; Relaxation (psychology) ; Fourier series ; Law ; Condensed matter physics ; Mathematics ; Mathematical analysis ; Mechanics ; Heat transfer ; Quantum mechanics Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Thermoelastic and Magnetoelastic Phenomena
Authors: Zhi Liu | Lei Teng | Laifeng Ma | Yang Liu | Xueying Zhang | Jialing Xue | Muhammad Ikram | Mohib Ullah | Li Li | Keying Shi
Source: RSC Advances (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Porous 3D flower-like CoAl-LDH nanocomposite with excellent performance for NO<sub>2</sub> detection at room temperature The 3D flower-like CoAl-layered double hydroxide (CoAl-LDH) was successfully prepared using the functional template agent of fluoride ions <i>via</i> a facile one-step hydrothermal route. Various techniques proved that all the samples presented 3D flower-like microstructural morphology. Representatively, the CA-2 sample, which was synthesized with the molar ratio of Co : Al of 3.65 : 1, had considerably abundant pores in its thin nanosheets. The average pore size was 2-4 nm, the specific surface area was equal to 49.45 m<sup>2</sup> g<sup>-1</sup>, and the thickness of nanosheets was approximately 3.068 nm. The CA-2 sample showed an excellent response to 0.01-100 ppm NO<sub>2</sub> with ultrafast response/recovery time at room temperature (RT). The detection limit of the sensor even reached 10 ppb. The superior gas sensing performance could be attributed to the synergistic effects of the functional template agent of fluoride ions and specific porous 3D flower-like nanostructure. The current study showed that the 3D flower-like CoAl-LDHs might a promising material in practical detection of NO<sub>2</sub> at RT. Materials science ; Hydroxide ; Detection limit ; Porosity ; Fluoride ; Hydrothermal circulation ; Nanocomposite ; Chemical engineering ; Ion ; Coal ; Nanostructure ; Specific surface area ; Nanoparticle ; Nanotechnology ; Catalysis ; Chromatography ; Composite material ; Inorganic chemistry ; Chemistry ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Advanced Photocatalysis Techniques ; Supercapacitor Materials and Fabrication
Authors: Tendai Samkange | Sarah D’Souza | Kenechukwu Obikeze | Admire Dube
Source: Journal of Pharmacy and Pharmacology (article, 2019-08-06)
Field: Materials Science — Biomaterials

Influence of PEGylation on PLGA nanoparticle properties, hydrophobic drug release and interactions with human serum albumin The ability to fine tune drug release and formation of protein corona around nanoparticles is crucial to formulation scientists. This study suggests that PLGA NPs with low PEG content might be suitable for extended circulation and rapid drug release and that higher PEG content retards hydrophobic drug release. PEGylation ; PLGA ; Drug ; Chemistry ; Nanoparticle ; Albumin ; Human serum albumin ; Drug carrier ; Pharmacology ; Chromatography ; Nanotechnology ; Medicine ; Materials science ; Polyethylene glycol ; Biochemistry Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Graphene and Nanomaterials Applications
Authors: Tassilo Gleede | Jens C. Markwart | Niklas Huber | Elisabeth Rieger | Frederik R. Wurm
Source: Macromolecules (article, 2019-12-09)
Field: Chemistry — Organic Chemistry

Competitive Copolymerization: Access to Aziridine Copolymers with Adjustable Gradient Strengths Competitive copolymerization gives access to gradient copolymers with simple one-step and one-pot strategies. Due to the living nature of the sulfonyl-aziridine polymerization, gradient copolymers can be obtained with low dispersities and adjustable molar masses. The combination of different sulfonyl activating groups allowed to fine-tune the reactivity difference of the comonomers and thus an exact adjustment of the gradient strength. Sulfonyl-activated aziridines are to date the only monomer class providing access to gradient copolymers with reactivity ratios ranging from (1 ≤ r1 ≤ 2; 1 ≥ r2 ≥ 0.5) for statistical or soft gradient copolymers to block copolymers (r1 ≥ 20, r2 ≤ 0.02), only by adjusting the electron-withdrawing effect of the activation groups: the reactivity ratios were calculated by different models for a library of eight comonomers. This library was further used to classify between hard, medium, and soft gradients. From the data obtained from the monomer library, it was possible to predict polymerization rate coefficients (kp) for aziridines, which were not prepared so far: correlation of the shifts in the 13C NMR spectra, the Hammett parameters and secondary parameters such as calculated lowest unoccupied molecular orbital (LUMO) levels of the monomers and the natural charge at the electrophilic carbon, etc., were used to predict (co)monomer reactivity and the resulting gradient strength. We believe that our findings allow us to access tailored gradient copolymers with a controlled monomer sequence distribution depending on the chemical control of comonomer reactivity. With these systematic data on activated aziridines, also more complex copolymer structures can be predicted and prepared. Such materials might find application as linear polyethylenimine derivatives to act as functional polyelectrolytes, or predesigned compatibilizers and surface-active gradient copolymers by a predictable one-step copolymerization. Copolymer ; Comonomer ; Reactivity (psychology) ; Monomer ; Polymer chemistry ; Chemistry ; Polymerization ; Sulfonyl ; Aziridine ; Materials science ; Polymer ; Organic chemistry ; Alkyl Synthetic Organic Chemistry Methods ; Fuel Cells and Related Materials ; Advanced Polymer Synthesis and Characterization
Authors: Ana M. García | Roy Lavendomme | Slavko Kralj | Marina Kurbasic | Ottavia Bellotto | Maria C. Cringoli | Sabrina Semeraro | Antonella Bandiera | Rita De Zorzi | Silvia Marchesan
Source: Chemistry - A European Journal (article, 2019-12-22)
Field: Materials Science — Biomaterials

Self‐Assembly of an Amino Acid Derivative into an Antimicrobial Hydrogel Biomaterial N-(4-Nitrobenzoyl)-Phe self-assembled into a transparent supramolecular hydrogel, which displayed high fibroblast and keratinocyte cell viability. The compound showed a mild antimicrobial activity against E. coli both as a hydrogel and in solution. Single-crystal XRD data revealed packing details, including protonation of the C-terminus due to an apparent pK<sub>a</sub> shift, as confirmed by pH titrations. MicroRaman analysis revealed almost identical features between the gel and crystal states, although more disorder in the former. The hydrogel is thermoreversible and disassembles within a range of temperatures that can be fine-tuned by experimental conditions, such as gelator concentration. At the minimum gelling concentration of 0.63 wt %, the hydrogel disassembles in a physiological temperature range of 39-42 °C, thus opening the way to its potential use as a biomaterial. Biomaterial ; Protonation ; Titration ; Self-healing hydrogels ; Supramolecular chemistry ; Antimicrobial ; Chemistry ; Fibroblast ; Derivative (finance) ; Polymer chemistry ; Materials science ; Nuclear chemistry ; Stereochemistry ; Crystal structure ; Crystallography ; Biochemistry ; Organic chemistry ; In vitro ; Ion Supramolecular Self-Assembly in Materials ; Antimicrobial Peptides and Activities
Authors: Coralie Lebleu | Laura C. Rodrigues | Jean‐Michel Guigner | Annie Brûlet | Élisabeth Garanger | Sébastien Lecommandoux
Source: Langmuir (article, 2019-09-24)
Field: Chemistry — Organic Chemistry

Self-Assembly of PEG-<i>b</i>-PTMC Copolymers: Micelles and Polymersomes Size Control Poly(ethylene glycol)<sub>45</sub>-<i>b</i>-poly(trimethylene carbonate)<sub><i>n</i></sub> PEG<sub>45</sub>-<i>b</i>-PTMC<sub><i>n</i></sub> diblock copolymers were synthesized with five different PTMC degrees of polymerization (<i>n</i> = 38, 96, 144, 170, 332) and their self-assembly properties in water were studied using a manual nanoprecipitation procedure. We confirmed that the copolymer's hydrophilic weight fraction (<i>f</i><sub>PEG</sub>) is controlling nanoparticles morphology. We determined that the PEG<sub>45</sub>-<i>b</i>-PTMC<sub>96</sub> with <i>f</i><sub>PEG</sub> ≈ 17% is the optimal hydrophilic fraction for the stabilization of well-defined unilamellar vesicles with a membrane thickness of δ ≈ 14.6 nm. Maintaining this fraction constant and modulating the overall molar mass of the block copolymers allowed the establishment of a power law of [Formula: see text] which provides a robust correlation between the molar mass of PTMC and vesicles' membrane thickness. Finally, we proved that controlling nanoprecipitation's conditions by microfluidics allowed fine-tuning and control of the nanoparticles size and polydispersity index while maintaining their shape with a perfect batch-to-batch reproducibility. Dispersity ; Polymersome ; Molar mass ; Copolymer ; Micelle ; Ethylene glycol ; Vesicle ; Nanoparticle ; Materials science ; Molar mass distribution ; PEG ratio ; Mass fraction ; Polymer chemistry ; Chemical engineering ; Membrane emulsification ; Membrane ; Volume fraction ; Chemistry ; Nanotechnology ; Amphiphile ; Organic chemistry ; Polymer ; Aqueous solution Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies ; Dendrimers and Hyperbranched Polymers
Authors: Anna Šimonová | Ivan Magriñá | Veronika Sýkorová | Radek Pohl | Mayreli Ortiz | Luděk Havran | Miroslav Fojta | Ciara K. O’Sullivan | Michal Hocek
Source: Chemistry - A European Journal (article, 2019-11-14)
Field: Chemistry — Organic Chemistry

Tuning of Oxidation Potential of Ferrocene for Ratiometric Redox Labeling and Coding of Nucleotides and DNA Three sets of 7-deazaadenine and cytosine nucleosides and nucleoside triphosphates bearing either unsubstituted ferrocene, octamethylferrocene and ferrocenecarboxamide linked through an alkyne tether to position 7 or 5, respectively, were designed and synthesized. The modified dN<sup>FcX</sup> TPs were good substrates for KOD XL DNA polymerase in primer extension and were used for enzymatic synthesis of redox-labelled DNA probes. Square-wave voltammetry showed that the octamethylferrocene oxidation potential was shifted to lower values, whilst the ferrocenecarboxamide was shifted to higher potentials, as compared to ferrocene. Tailed PEX products containing different ratios of Fc-labelled A (dA<sup>Fc</sup> ) and FcPa-labelled C (dC<sup>FcPa</sup> ) were synthesized and hybridized with capture oligonucleotides immobilized on gold electrodes to study the electrochemistry of the redox-labelled DNA. Clearly distinguishable, fully orthogonal and ratiometric peaks were observed for the dA<sup>Fc</sup> and dC<sup>FcPa</sup> bases in DNA, demonstrating their potential for use in redox coding of nucleobases and for the direct electrochemical measurement of the relative ratio of nucleobases in an unknown sequence of DNA. Ferrocene ; Redox ; Nucleotide ; DNA ; Chemistry ; DNA oxidation ; Combinatorial chemistry ; Oxidation reduction ; Biophysics ; Photochemistry ; Biochemistry ; Electrochemistry ; DNA damage ; Inorganic chemistry ; Biology ; Electrode ; Gene Ferrocene Chemistry and Applications ; Advanced biosensing and bioanalysis techniques ; DNA and Nucleic Acid Chemistry