jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Audrey A. Burnim | Da Xu | Matthew A. Spence | Colin J. Jackson | Nozomi Ando
Source: Protein Science (article, 2022-10-29)
Field: Chemistry — Inorganic Chemistry

Analysis of insertions and extensions in the functional evolution of the ribonucleotide reductase family Ribonucleotide reductases (RNRs) are used by all free-living organisms and many viruses to catalyze an essential step in the de novo biosynthesis of DNA precursors. RNRs are remarkably diverse by primary sequence and cofactor requirement, while sharing a conserved fold and radical-based mechanism for nucleotide reduction. In this work, we expand on our recent phylogenetic inference of the entire RNR family and describe the evolutionarily relatedness of insertions and extensions around the structurally homologous catalytic barrel. Using evo-velocity and sequence similarity network (SSN) analyses, we show that the N-terminal regulatory motif known as the ATP-cone domain was likely inherited from an ancestral RNR. By combining SSN analysis with AlphaFold2 predictions, we also show that the C-terminal extensions of class II RNRs can contain folded domains that share homology with an Fe-S cluster assembly protein. Finally, using sequence analysis and AlphaFold2, we show that the sequence motif of a catalytically essential insertion known as the finger loop is tightly coupled to the catalytic mechanism. Based on these results, we propose an evolutionary model for the diversification of the RNR family. Biology ; Sequence motif ; Genetics ; Nucleotide ; Homing endonuclease ; Phylogenetic tree ; Computational biology ; Sequence alignment ; Homology (biology) ; Structural motif ; Multiple sequence alignment ; Peptide sequence ; DNA ; Amino acid ; Gene ; Biochemistry ; Endonuclease Metal-Catalyzed Oxygenation Mechanisms ; RNA and protein synthesis mechanisms ; Genomics and Phylogenetic Studies
Authors: Marwa A. Fouad | Ahmed Serag | Enas H. Tolba | Manal A. El‐Shal | Ahmed M. El Kerdawy
Source: BMC Chemistry (article, 2022-11-03)
Field: Chemistry — Spectroscopy

QSRR modeling of the chromatographic retention behavior of some quinolone and sulfonamide antibacterial agents using firefly algorithm coupled to support vector machine Abstract Quinolone and sulfonamide are two classes of antibacterial agents with an opulent history of medicinal chemistry features that contribute to their bacterial spectrum, efficacy, pharmacokinetics, and adverse effect profiles. The urgent need for their use, combined with the escalating rate of their resistance, necessitates the development of suitable analytical methods that accelerate and facilitate their analysis. In this study, the advanced firefly algorithm (FFA) coupled with support vector regression (SVR) was used to select the most significant descriptors and to construct two quantitative structure-retention relationship (QSRR) models using a series of 11 selected quinolone and 13 sulfonamide drugs, respectively, to predict their retention behavior in HPLC. Precisely, the effect of the pH value and acetonitrile composition in the mobile phase on the retention behavior of quinolones and sulfonamides, respectively, were studied. The obtained QSRR models performed well in both internal and external validations, demonstrating their robustness and predictive ability. Y-randomization validation demonstrated that the obtained models did not result by statistical chance. Moreover, the obtained results shed the light on the molecular features that influence the retention behavior of these two classes under the current chromatographic conditions. Sulfonamide ; Support vector machine ; Quinolone ; Chemistry ; Retention time ; Acetonitrile ; Chromatography ; Computer science ; Machine learning ; Antibiotics ; Stereochemistry Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods
Authors: Benjamin A. Helfrecht | Giovanni Pireddu | Rocío Semino | Scott M. Auerbach | Michele Ceriotti
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Ranking the synthesizability of hypothetical zeolites with the sorting hat Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme-the "Zeolite Sorting Hat"-that exploits interatomic correlations to discriminate between real and hypothetical zeolites and to partition real zeolites into compositional classes that guide synthetic strategies for a given hypothetical framework. We find that, regardless of the structural descriptor used by the Zeolite Sorting Hat, there remain hypothetical frameworks that are incorrectly classified as real ones, suggesting that they might be good candidates for synthesis. We seek to minimize the number of such misclassified frameworks by using as complete a structural descriptor as possible, thus focusing on truly viable synthetic targets, while discovering structural features that distinguish real and hypothetical frameworks as an output of the Zeolite Sorting Hat. Further ranking of the candidates can be achieved based on thermodynamic stability and/or their suitability for the desired applications. Based on this workflow, we propose three hypothetical frameworks differing in their molar volume range as the top targets for synthesis, each with a composition suggested by the Zeolite Sorting Hat. Finally, we analyze the behavior of the Zeolite Sorting Hat with a hierarchy of structural descriptors including intuitive descriptors reported in previous studies, finding that intuitive descriptors produce significantly more misclassified hypothetical frameworks, and that more rigorous interatomic correlations point to second-neighbor Si-O distances around 3.2-3.4 Å as the key discriminatory factor. Zeolite ; Sorting ; Computer science ; Haystack ; Ranking (information retrieval) ; Workflow ; Sorting algorithm ; Artificial intelligence ; Algorithm ; Chemistry ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Zeolite Catalysis and Synthesis
Authors: Raana Sabri | Hossein Mosallaei
Source: Nanophotonics (article, 2022-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of perimeter‐controlled InAs‐assisted metasurface for two‐dimensional dynamic beam steering The current commercially viable light detection and ranging systems demand continuous, full-scene, and dynamic two-dimensional point scanning, while featuring large aperture size to ensure long distance operation. However, the biasing architecture of large-area arrays with numerous individually controlled tunable elements is substantially complicated. Herein, inverse design of a perimeter-controlled active metasurface for two-dimensional dynamic beam steering at mid-infrared regime is theoretically presented. The perimeter-control approach simplifies biasing architecture by allowing column-row addressing of the elements. The metasurface consists of a periodic array of plasmonic patch nanoantennas in a metal-insulator-metal configuration, wherein two active layers of indium arsenide are incorporated into its building block. The metasurface profile facilitates wide phase modulation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>≈</mml:mo> <mml:mn>355</mml:mn> <mml:mo>°</mml:mo></mml:math> on the reflected light at the individual element level through applying independent voltages to its respective columns and rows. The multi-objective genetic algorithm (GA) for optimizing user-defined metrics toward shaping desired far-zone radiation pattern is implemented. It is demonstrated that multi-objective GA yields better results for directivity and spatial resolution of perimeter-controlled metasurface by identifying the design tradeoffs inherent to the system, compared to the single-objective optimizer. A high directivity and continuous beam scanning with full and wide field-of-view along the azimuth and elevation angles are respectively maintained. Directivity ; Optics ; Aperture (computer memory) ; Beam steering ; Plasmon ; Biasing ; Row ; Materials science ; Computer science ; Optoelectronics ; Voltage ; Physics ; Beam (structure) ; Electrical engineering ; Telecommunications ; Engineering ; Acoustics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Optical Wireless Communication Technologies
Authors: Anjitha Jacob | I.P. Sudagar | R. Pandiselvam | P. Rajkumar | M. Rajavel
Source: Quality Assurance and Safety of Crops & Foods (article, 2022-11-16)
Field: Chemistry — Inorganic Chemistry

Optimization of ultrasound processing parameters for preservation of matured coconut water using a central composite design Mature coconut water (MCW) is a natural beverage and a main by-product of various coconut processing industries such as virgin coconut oil, coconut chips, coconut milk, etc. In spite of huge benefits, MCW’s short shelf life limits its market potential. In this context, the present study investigates the effect of ultrasonic processing parameters, such as amplitude (50%, 60% and 70%) and time (5 min, 10 min and 15 min), on microbial population and quality profile (pH, total soluble solids, total sugars, reducing sugars and non-reducing sugars) of MCW. Central composite design was used to create a multiple linear regression model for each response and to optimize ultrasound processing parameters. The optimal treatment parameters to ensure microbial safety and preserve the nutritional quality of MCW were 60% ultrasonic amplitude and treatment time of 10 min. Total sugars, reducing sugars, non-reducing sugars and microbial load of MCW determined at optimized conditions were 4.92%, 2.804%, 2.13% and 4.79 log cfu/mL, respectively. Ultrasonic treatment was found to be effective in inhibiting microbial growth and maintaining non-reducing sugars of MCW. Food science ; Central composite design ; Chemistry ; Context (archaeology) ; Shelf life ; Response surface methodology ; Pulp and paper industry ; Population ; Ultrasonic sensor ; Biology ; Chromatography ; Engineering Coconut Research and Applications ; Radiation Effects and Dosimetry
Authors: Yulun Wu | Tobias Wood | Fatemeh Arzanforoosh | Juan A. Hernández‐Tamames | Gareth J. Barker | Marion Smits | Esther A. H. Warnert
Source: Magnetic Resonance Materials in Physics Biology and Medicine (article, 2022-01-07)
Field: Materials Science — Materials Chemistry

3D APT and NOE CEST-MRI of healthy volunteers and patients with non-enhancing glioma at 3 T This work showed that with a rapid CEST acquisition using a B<sub>1</sub> saturation power of 1.5 µT and covering the whole tumor, analysis of both LD APT and MTR<sub>REX</sub> NOE allows for observing tumor heterogeneity, which will be beneficial in future studies using CEST-MRI to improve imaging diagnostics for non-enhancing glioma. Glioma ; Nuclear magnetic resonance ; White matter ; Nuclear medicine ; Chemistry ; Magnetic resonance imaging ; Magnetization transfer ; Medicine ; Radiology ; Physics ; Cancer research Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Suci Ashari | Gomal Juni Yanris | Iwan Purnama
Source: SinkrOn (article, 2022-05-02)
Field: Chemistry — Inorganic Chemistry

Oil Palm Fruit Ripeness Detection using Deep Learning To detect the maturity level of oil palm Fresh Fruit Bunches (FFB) generally seen from the loose fruit that fell to the ground. This method is always used when harvesting swit coconuts. Even though this method is not always valid, because many factors cause the fruit to fall from the bunch. The manual harvesting process can result in the quality of palm oil being not optimal. For this reason, technology is needed that can ensure the maturity level of oil palm FFB. This study aims to detect the maturity of oil palm FFB based on digital images by applying a deep learning algorithm so that the maturity level can be classified into three categories, namely: raw, ripe, and rotten. The deep learning algorithm was chosen because there have been many studies that have proven its high level of accuracy. This research method starts from; preparation of data, designing architectural models and convolutional neural network parameters, testing models, testing images, and analyzing results. From the results of the study, it was found that the convolutional neural network algorithm can be applied to detect the maturity level of oil palm FFB with an accuracy value of 92% for test data, and 76% for model testing. Ripeness ; Palm oil ; Convolutional neural network ; Maturity (psychological) ; Palm ; Computer science ; Deep learning ; Artificial intelligence ; Artificial neural network ; Agricultural engineering ; Machine learning ; Mathematics ; Horticulture ; Environmental science ; Engineering ; Agroforestry ; Biology Coconut Research and Applications ; Oil Palm Production and Sustainability ; Agricultural and Environmental Management
Authors: Guosheng Zhang | Tongyu Xu | Youwen Tian | Shuai Feng | Dongxue Zhao | Zhonghui Guo
Source: Scientific Reports (article, 2022-11-17)
Field: Chemistry — Analytical Chemistry

Classification of rice leaf blast severity using hyperspectral imaging Rice leaf blast is prevalent worldwide and a serious threat to rice yield and quality. Hyperspectral imaging is an emerging technology used in plant disease research. In this study, we calculated the standard deviation (STD) of the spectral reflectance of whole rice leaves and constructed support vector machine (SVM) and probabilistic neural network (PNN) models to classify the degree of rice leaf blast at different growth stages. Average accuracies at jointing, booting and heading stages under the full-spectrum-based SVM model were 88.89%, 85.26%, and 87.32%, respectively, versus 80%, 83.16%, and 83.41% under the PNN model. Average accuracies at jointing, booting and heading stages under the STD-based SVM model were 97.78%, 92.63%, and 92.20%, respectively, versus 88.89%, 91.58%, and 92.20% under the PNN model. The STD of the spectral reflectance of the whole leaf differed not only within samples with different disease grades, but also among those at the same disease level. Compared with raw spectral reflectance data, STDs performed better in assessing rice leaf blast severity. Hyperspectral imaging ; Rice plant ; Support vector machine ; Reflectivity ; Heading (navigation) ; Probabilistic neural network ; Remote sensing ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Artificial neural network ; Horticulture ; Biology ; Engineering ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Zisheng Liao | Jie Yang | Mokarram Hossain | Grégory Chagnon | Xiaohu Yao
Source: International Journal of Non-Linear Mechanics (article, 2022-12-24)
Field: Materials Science — Polymers and Plastics

The time and temperature dependences of the stress recovery of Ecoflex polymer Ecoflex silicone rubbers, served as the polymeric components of flexible electronic devices, actuators, energy converters, etc., are subjected to repetitive loadings during their service lives. The resulting stress softening and stress recovery phenomena can impose complicated impacts on their actual performances. Therefore, the importance of a better understanding of these phenomena needs to be stressed. In this contribution, we focus on the stress recovery behaviour of the Ecoflex silicone rubber in consideration of time and temperature dependences. Our findings show the time–temperature equivalence of the stress recovery behaviour. Based on the experimental study, a constitutive model is developed at finite strains using the Time–Temperature Superposition (TTS) principle to capture both the time and temperature dependences of the stress recovery behaviour of Ecoflex. Materials science ; Silicone rubber ; Stress (linguistics) ; Elastomer ; Composite material Polymer Nanocomposites and Properties ; Elasticity and Material Modeling ; Advanced Sensor and Energy Harvesting Materials
Authors: Zhiyong Zou | Jie Chen | Man Zhou | Yongpeng Zhao | Long Tao | Qingsong Wu | Lijia XU
Source: Food Science and Technology (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Prediction of peanut seed vigor based on hyperspectral images Prediction of seed vigor based on hyperspectral peant. The traditional method is time-consuming and laborious to detect seed vigor. At the same time, the accuracy of the detection result is not high, and it will cause damage to the seed itself. Therefore, in order to achieve rapid and non-destructive detection of peanut seed vigor, the test was performed with original health, artificial aging for 24h and Peanut seeds with different vigor gradients at 72 hours were used as the research samples. Hyperspectral images with a wavelength range of 387~1035 nm were collected, and the image of the central part of the peanut seeds with a pixel size of 60 × 60 after correction was intercepted and the average reflectance value was calculated. After a combination of processing analysis, characteristic band processing, and model selection, a hyperspectral prediction system with the highest correlation to the viability of extracted peanut seeds was finally established. Experiments shown that the combination of hyperspectral imaging technology and the MF-LightGBM-RF model had the best performance, with a prediction accuracy of 92.59% and a fitting time of 1.77s, which simplifies the model and improves efficiency. Hyperspectral imaging ; Pixel ; Artificial intelligence ; Mathematics ; Pattern recognition (psychology) ; Biological system ; Computer science ; Remote sensing ; Biology ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Advanced Chemical Sensor Technologies
Authors: Jia Ying Tan | Wah Yen Tey | Joongjai Panpranot | Steven Lim | Kiat Moon Lee
Source: Sustainability (article, 2022-09-13)
Field: Materials Science — Biomaterials

Valorization of Oil Palm Empty Fruit Bunch for Cellulose Fibers: A Reinforcement Material in Polyvinyl Alcohol Biocomposites for Its Application as Detergent Capsules Cellulose fibers isolated from oil palm empty fruit bunches (OPEFB) have been studied as a potential reinforcement for polyvinyl alcohol (PVA) biocomposite. Analysis of variance (ANOVA) showed that all three parameters—hydrolysis temperature, time and acid concentration, as well as their interactions—significantly affected the yield of cellulose. Moving Least Squares (MLS) and Multivariable Power Least Squares (MPLS) models demonstrated good fitness. The model also proved that acid concentration was the dominant parameter, supported by the Fourier transform infrared spectroscopy (FTIR) analysis. Hydrolysis using 54% acid at 35 °C and 15 min achieved the highest cellulose yield of 80.72%. Cellulose-reinforced PVA biocomposite films demonstrated better mechanical strength, elongation at break, moisture barrier properties, thermal stability and poorer light transmission rate compared to neat PVA due to the high aspect ratio, crystallinity and good compatibility of cellulose fibers. These findings suggested the potential of cellulose fibers-reinforced PVA biocomposite film as water-soluble detergent capsules. Cellulose ; Biocomposite ; Polyvinyl alcohol ; Materials science ; Fourier transform infrared spectroscopy ; Hydrolysis ; Composite material ; Crystallinity ; Thermal stability ; Lignin ; Acid hydrolysis ; Chemical engineering ; Nuclear chemistry ; Polymer chemistry ; Composite number ; Chemistry ; Organic chemistry Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging
Authors: Meiyan Shu | Long Zhou | Haochong Chen | Xiqing Wang | Lei Meng | Yuntao Ma
Source: Frontiers in Plant Science (article, 2022-08-03)
Field: Chemistry — Analytical Chemistry

Estimation of amino acid contents in maize leaves based on hyperspectral imaging Estimation of the amino acid content in maize leaves is helpful for improving maize yield estimation and nitrogen use efficiency. Hyperspectral imaging can be used to obtain the physiological and biochemical parameters of maize leaves with the advantages of being rapid, non-destructive, and high throughput. This study aims to estimate the multiple amino acid contents in maize leaves using hyperspectral imaging data. Two nitrogen (N) fertilizer experiments were carried out to obtain the hyperspectral images of fresh maize leaves. The partial least squares regression (PLSR) method was used to build the estimation models of various amino acid contents by using the reflectance of all bands, sensitive band range, and sensitive bands. The models were then validated with the independent dataset. The results showed that (1) the spectral reflectance of most amino acids was more sensitive in the range of 400-717.08 nm than other bands. The estimation accuracy was better by using the reflectance of the sensitive band range than that of all bands; (2) the sensitive bands of most amino acids were in the ranges of 505.39-605 nm and 651-714 nm; and (3) among the 24 amino acids, the estimation models of the β-aminobutyric acid, ornithine, citrulline, methionine, and histidine achieved higher accuracy than those of other amino acids, with the <i>R</i> <sup>2</sup>, relative root mean square error (RE), and relative percent deviation (RPD) of the measured and estimated value of testing samples in the range of 0.84-0.96, 8.79%-19.77%, and 2.58-5.18, respectively. This study can provide a non-destructive and rapid diagnostic method for genetic sensitive analysis and variety improvement of maize. Hyperspectral imaging ; Biology ; Botany ; Agronomy ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture