jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hua Tian | Presha Rajbhandari | Jay G. Tarolli | Aubrianna Decker | Taruna V. Neelakantan | Tina B. Angerer | Fereshteh Zandkarimi | Jacob Daniels | Helen Remotti | Gilles Frache | Nicholas Winograd | Brent R. Stockwell
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-09-28)
Field: Chemistry — Spectroscopy

Multi-modal mass spectrometry imaging reveals single-cell metabolic states in mammalian liver Summary We have developed a powerful workflow to imaging endogenous metabolism in single cells on frozen tissue, allowing us to discover new cell subtypes in human liver. Performing spatially integrated multiomics in single cells within tissues is at the leading frontier in biology but has been prevented by technological challenges. We developed a critical new technology, cryogenic water cluster ion beam secondary ion mass spectrometry imaging ((H 2 O) n&gt;28K -GCIB-SIMS)) at 1 µm single-cell resolution. This allowed us to perform multi-modal mass spectrometry imaging (MSI) to detect metabolites, lipids, and proteins in single cells within functional liver zones and diverse cell types in the native tissue state. Our workflow utilizes the desorption electrospray ionization (DESI) mass spectrometry imaging (MSI) to build a reference map of metabolic heterogeneity and zonation across liver functional units. Then cryogenic (H 2 O) n&gt;28K -GCIB-SIMS and C 60 -SIMS integrated metabolomics, lipidomic and proteomics, - characterizing the metabolic state in single cells on the same tissue section. We found for the first time that lipids and metabolites can classify liver metabolic zones and liver cell types beyond histological and protein-marker annotation. This provides a multi-modal workflow to define single-cell states in normal physiology and disease in mammalian tissue. Mass spectrometry imaging ; Mass spectrometry ; MALDI imaging ; Proteomics ; Metabolomics ; Chemistry ; Cell ; Single-cell analysis ; Secondary ion mass spectrometry ; Computational biology ; Biochemistry ; Biology ; Desorption ; Matrix-assisted laser desorption/ionization ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Ion-surface interactions and analysis
Authors: Sadaf Shakoor | Amin Ismail | Zia-ur-Rahman | Mohd Redzwan Sabran | Alaa El‐Din A. Bekhit | Shahin Roohinejad
Source: International Food Research Journal (article, 2022-02-01)
Field: Chemistry — Analytical Chemistry

Impact of tartrazine and curcumin on mineral status, and thyroid and reproductive hormones disruption in vivo Endocrine disrupters (EDCs) are naturally occurring or man-made substances that either mimic or obstruct the functions of oestrogens and androgens, thyroid hormones, as well as microminerals in the body. The present work aimed to evaluate the effects of oral administration of tartrazine and curcumin, synthetic and natural dyes, respectively, on thyroid hormones (T3, T4, and TSH), female reproductive hormones (oestrogen, progesterone, LH, and FSH), and minerals (iron, copper, zinc, sodium, potassium, and chloride) in plasma, liver, and kidney of female rats after 15, 30, and 45 d of treatment. The rats were treated with admissible daily intake (ADI) and 10× ADI (9.6 and 96 mg/kg/body weight for tartrazine, 3.85 and 38.5 mg/kg/body weight for curcumin, respectively). Results showed significant changes in thyroid and female reproductive hormones, especially, in the tartrazine-treated groups as compared to the control. Low and high doses of tartrazine and curcumin significantly (p &lt; 0.05) decreased iron, copper, and zinc concentrations in plasma, whereas, the concentrations of sodium and copper in liver and kidney increased. Both tartrazine and curcumin, at ADI and 10× ADI, resulted in lower LH levels after 30 and 45 d of treatment. After 30 d, low and high dose of tartrazine significantly decreased T4, oestrogen, and FSH levels; whereas, progesterone level increased. The results demonstrated that hormone secretion and mineral content in tissues are severely affected at ADI and higher concentrations of tartrazine and curcumin. These observations suggested that lower doses of these dyes might be a safer option for their usage in foods and pharmaceuticals. Tartrazine ; Endocrinology ; Chemistry ; Hormone ; Internal medicine ; Curcumin ; Thyroid ; Kidney ; Medicine ; Biochemistry Dye analysis and toxicity ; Biochemical Analysis and Sensing Techniques ; melanin and skin pigmentation
Authors: Laurianne David | Anissa Mdahoma | Natesh Singh | Sébastien Buchoux | Émilie Pihan | Constantino Diaz | Obdulia Rabal
Source: Bioinformatics Advances (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

A toolkit for covalent docking with GOLD: from automated ligand preparation with KNIME to bound protein–ligand complexes Supplementary data are available at <i>Bioinformatics Advances</i> online. Covalent bond ; Workflow ; Computer science ; Docking (animal) ; Python (programming language) ; Ligand (biochemistry) ; Virtual screening ; Chemistry ; Drug discovery ; Combinatorial chemistry ; Operating system ; Biochemistry ; Database ; Organic chemistry Click Chemistry and Applications ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Irivette Dominguez-Martinez | Freisa M. Joaquín-Ovalle | Yancy Ferrer‐Acosta | Kai Griebenow
Source: Pharmaceutics (article, 2022-02-24)
Field: Materials Science — Biomaterials

Folate-Decorated Cross-Linked Cytochrome c Nanoparticles for Active Targeting of Non-Small Cell Lung Carcinoma (NSCLC) The folate receptor alpha (FR), which is overexpressed in solid tumors including NSCLC, can be utilized for active tumor targeting to afford more effective cancer therapies. In this context, cytochrome c (Cyt c) has drawn attention to cancer research because it is non-toxic, yet, when delivered to the cytoplasm of cancer cells, can kill them by inducing apoptosis. Cyt c nanoparticles (NPs, 169 ± 9 nm) were obtained by solvent precipitation with acetonitrile, and stabilized by reversible homo-bifunctional crosslinking to accomplish a Cyt-c-based drug delivery system that combines stimulus-responsive release and active targeting. Cyt c was released under intracellular redox conditions, due to an S-S bond in the NPs linker, while NPs remained intact without any release under extracellular conditions. The NP surface was decorated with a hydrophilic folic acid-polyethylene glycol (FA-PEG) polymer for active targeting. The FA-decorated NPs specifically recognized and killed cancer cells (IC<sub>50</sub> = 47.46 µg/mL) that overexpressed FR, but showed no toxicity against FR-negative cells. Confocal microscopy confirmed the preferential uptake and apoptosis induction of our NPs by FR-positive cancer cells. In vivo experiments using a Lewis lung carcinoma (LLC) mouse model showed visible NP accumulation within the tumor and inhibited the growth of LLC tumors. Chemistry ; Cancer research ; Lung ; Small Cell Lung Carcinoma ; Cytochrome c ; Medicine ; Small-cell carcinoma ; Apoptosis ; Internal medicine ; Biochemistry Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Cancer Research and Treatments
Authors: Muhammad Abid Anwar | Munir Ali | Dong Pu | Srikrishna Chanakya Bodepudi | Jianhang Lv | Khurram Shehzad | Xiaochen Wang | Ali Imran | Yuda Zhao | Shurong Dong | Huan Hu | Bin Yu | Yang Xu
Source: IEEE Journal of the Electron Devices Society (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Graphene–Silicon Diode for 2-D Heterostructure Electrical Failure Protection Two-dimensional materials have modernized a broad interest in electronic devices. Along with many advantages, their atomic-level thickness makes them sensitive under high electrical stress. This work proposes a protection design using a Graphene/Silicon (Gr/Si) Schottky diode as the protective device, which helps to improve the endurance for unwanted fluctuations in operating voltage of 2D heterostructure-based devices. In this scheme, the 2D heterostructure was configured parallel with the protective device (Gr/Si diode) for electrical measurements. It was found that Gr/Si diode handles a large portion of initial surge current peaks, which significantly increases the durability and lifetime of 2D material-based heterostructure devices. This scheme potentially bridges mature CMOS technology and novel 2D-based heterostructure applications for robust futuristic devices. Heterojunction ; Materials science ; Schottky diode ; Optoelectronics ; Diode ; Silicon ; CMOS ; Semiconductor device ; Schottky barrier ; Graphene ; Electrical engineering ; Electronic engineering ; Engineering physics ; Nanotechnology ; Engineering Graphene research and applications ; 2D Materials and Applications ; Nanowire Synthesis and Applications
Authors: Qunfeng Niu | Jiangpeng Liu | Yi Jin | Xia Chen | Wenkui Zhu | Qiang Yuan
Source: Frontiers in Plant Science (article, 2022-08-18)
Field: Chemistry — Organic Chemistry

Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision The primary task in calculating the tobacco shred blending ratio is identifying the four tobacco shred types: expanded tobacco silk, cut stem, tobacco silk, and reconstituted tobacco shred. The classification precision directly affects the subsequent determination of tobacco shred components. However, the tobacco shred types, especially expanded tobacco silk and tobacco silk, have no apparent differences in macro-scale characteristics. The tobacco shreds have small size and irregular shape characteristics, creating significant challenges in their recognition and classification based on machine vision. This study provides a complete set of solutions aimed at this problem for screening tobacco shred samples, taking images, image preprocessing, establishing datasets, and identifying types. A block threshold binarization method is used for image preprocessing. Parameter setting and method performance are researched to obtain the maximum number of complete samples with acceptable execution time. ResNet50 is used as the primary classification and recognition network structure. By increasing the multi-scale structure and optimizing the number of blocks and loss function, a new tobacco shred image classification method is proposed based on the MS-X-ResNet (Multi-Scale-X-ResNet) network. Specifically, the MS-ResNet network is obtained by fusing the multi-scale Stage 3 low-dimensional and Stage 4 high-dimensional features to reduce the overfitting risk. The number of blocks in Stages 1-4 are adjusted from the original 3:4:6:3 to 3:4:N:3 (A-ResNet) and 3:3:N:3 (B-ResNet) to obtain the X-ResNet network, which improves the model's classification performance with lower complexity. The focal loss function is selected to reduce the impact of identification difficulty for different sample types on the network and improve its performance. The experimental results show that the final classification accuracy of the network on a tobacco shred dataset is 96.56%. The image recognition of a single tobacco shred requires 103 ms, achieving high classification accuracy and efficiency. The image preprocessing and deep learning algorithms for tobacco shred classification and identification proposed in this study provide a new implementation approach for the actual production and quality detection of tobacco and a new way for online real-time type identification of other agricultural products. Overfitting ; Preprocessor ; Artificial intelligence ; Computer science ; Residual neural network ; Pattern recognition (psychology) ; Scale (ratio) ; Tobacco leaf ; Block (permutation group theory) ; Artificial neural network ; Mathematics ; Engineering ; Cartography ; Geography Wood and Agarwood Research ; Spectroscopy and Chemometric Analyses ; Advanced Image Fusion Techniques
Authors: Nuno Rodrigues | Nuno Ferreiro | Ana C. A. Veloso | José Alberto Pereira | António M. Peres
Source: Sensors (article, 2022-12-09)
Field: Chemistry — Organic Chemistry

An Electronic Nose as a Non-Destructive Analytical Tool to Identify the Geographical Origin of Portuguese Olive Oils from Two Adjacent Regions The geographical traceability of extra virgin olive oils (EVOO) is of paramount importance for oil chain actors and consumers. Oils produced in two adjacent Portuguese regions, Côa (36 oils) and Douro (31 oils), were evaluated and fulfilled the European legal thresholds for EVOO categorization. Compared to the Douro region, oils from Côa had higher total phenol contents (505 versus 279 mg GAE/kg) and greater oxidative stabilities (17.5 versus 10.6 h). The majority of Côa oils were fruity-green, bitter, and pungent oils. Conversely, Douro oils exhibited a more intense fruity-ripe and sweet sensation. Accordingly, different volatiles were detected, belonging to eight chemical families, from which aldehydes were the most abundant. Additionally, all oils were evaluated using a lab-made electronic nose, with metal oxide semiconductor sensors. The electrical fingerprints, together with principal component analysis, enabled the unsupervised recognition of the oils' geographical origin, and their successful supervised linear discrimination (sensitivity of 98.5% and specificity of 98.4%; internal validation). The E-nose also quantified the contents of the two main volatile chemical classes (alcohols and aldehydes) and of the total volatiles content, for the studied olive oils split by geographical origin, using multivariate linear regression models (0.981 ≤ <i>R</i><sup>2</sup> ≤ 0.998 and 0.40 ≤ RMSE ≤ 2.79 mg/kg oil; internal validation). The E-nose-MOS was shown to be a fast, green, non-invasive and cost-effective tool for authenticating the geographical origin of the studied olive oils and to estimate the contents of the most abundant chemical classes of volatiles. Electronic nose ; Portuguese ; Olive oil ; Engineering ; Computer science ; Materials science ; Nanotechnology ; Biology ; Food science ; Linguistics Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Varun Khemani | Michael H. Azarian | Michael Pecht
Source: Eng—Advances in Engineering (article, 2022-08-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Efficient Identification of Jiles–Atherton Model Parameters Using Space-Filling Designs and Genetic Algorithms The Jiles–Atherton model is widespread in the hysteresis description of ferromagnetic, ferroelectric, magneto strictive, and piezoelectric materials. However, the determination of model parameters is not straightforward because the model involves numerical integration and the solving of ordinary differential equations, both of which are error prone. As a result, stochastic optimization techniques have been used to explore the vast ranges of these parameters in an effort to identify the parameter values that minimize the error differential between experimental and modelled hysteresis curves. Because of the time-consuming nature of these optimization techniques, this paper explores the design space of the parameters using a space-filling design. This design provides a narrower range of parameters to look at with optimization algorithms, thereby reducing the time required to identify the optimal Jiles–Atherton model parameters. This procedure can also be carried out without using expensive hysteresis measurement devices, provided the desired transformer’s secondary voltage is known. Transformer ; Hysteresis ; Computer science ; Algorithm ; Genetic algorithm ; Ordinary differential equation ; Parameter space ; Voltage ; Optimization algorithm ; Range (aeronautics) ; Mathematical optimization ; Mathematics ; Control theory (sociology) ; Differential equation ; Engineering ; Physics ; Mathematical analysis ; Electrical engineering ; Statistics Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Non-Destructive Testing Techniques
Authors: Peter Sagmeister | Robin Hierzegger | Jason D. Williams | C. Oliver Kappe | Stefan Kowarik
Source: Digital Discovery (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Artificial neural networks and data fusion enable concentration predictions for inline process analytics Artificial neural networks (ANNs) facilitate rapid quantification of process spectra from inline process analytical technologies. Data fusion also enables combination of multiple data sources, resulting in better quality concentration measurements. Artificial neural network ; Analytics ; Computer science ; Process (computing) ; Data analysis ; Sensor fusion ; Fusion ; Artificial intelligence ; Data mining ; Data science ; Machine learning Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Chemical Sensor Technologies
Authors: Yann Lanoiselée | A. A. Stanislavsky | Davide Calebiro | Aleksander Weron
Source: Physical review. E (article, 2022-12-20)
Field: Chemistry — Physical and Theoretical Chemistry

Temperature and friction fluctuations inside a harmonic potential In this article we study the trapped motion of a molecule undergoing diffusivity fluctuations inside a harmonic potential. For the same diffusing-diffusivity process, we investigate two possible interpretations. Depending on whether diffusivity fluctuations are interpreted as temperature or friction fluctuations, we show that they display drastically different statistical properties inside the harmonic potential. We compute the characteristic function of the process under both types of interpretations and analyze their limit behavior. Based on the integral representations of the processes we compute the mean-squared displacement and the normalized excess kurtosis. In the long-time limit, we show for friction fluctuations that the probability density function (PDF) always converges to a Gaussian whereas in the case of temperature fluctuations the stationary PDF can display either Gaussian distribution or generalized Laplace (Bessel) distribution depending on the ratio between diffusivity and positional correlation times. Kurtosis ; Thermal diffusivity ; Probability density function ; Statistical physics ; Bessel function ; Gaussian ; Skewness ; Brownian motion ; Limit (mathematics) ; Laplace transform ; Thermal fluctuations ; Displacement (psychology) ; Correlation function (quantum field theory) ; Harmonic ; Mean squared displacement ; Physics ; Probability distribution ; Mathematics ; Thermodynamics ; Mathematical analysis ; Statistics ; Quantum mechanics ; Spectral density ; Molecular dynamics thermodynamics and calorimetric analyses ; stochastic dynamics and bifurcation ; Advanced Thermodynamics and Statistical Mechanics
Authors: Yanwen Zhang
Source: Nature Materials (article, 2022-05-31)
Field: Materials Science — Materials Chemistry

Reassembled nanoprecipitates resisting radiation Materials science ; Supersaturation ; Radiation resistance ; Superlattice ; Radiation ; Radiation damage ; Irradiation ; Chemical physics ; Nanotechnology ; Optoelectronics ; Optics ; Chemistry ; Thermodynamics ; Physics ; Nuclear physics Nuclear materials and radiation effects ; Nuclear Materials and Properties ; Fusion materials and technologies
Authors: Sizhou Chen | Jiazhi Shen | Kai Fan | Wenjun Qian | Honglian Gu | Yuchen Li | Jie Zhang | Xiao Han | Yu Wang | Zhaotang Ding
Source: Frontiers in Plant Science (article, 2022-12-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral machine-learning model for screening tea germplasm resources with drought tolerance Drought tolerance and quality stability are important indicators to evaluate the stress tolerance of tea germplasm resources. The traditional screening method of drought resistant germplasm is mainly to evaluate by detecting physiological and biochemical indicators of tea plants under drought stresses. However, the methods are not only time consuming but also destructive. In this study, hyperspectral images of tea drought phenotypes were obtained and modeled with related physiological indicators. The results showed that: (1) the information contents of malondialdehyde, soluble sugar and total polyphenol were 0.21, 0.209 and 0.227 respectively, and the drought tolerance coefficient (DTC) index of each tea variety was between 0.069 and 0.81; (2) the comprehensive drought tolerance of different varieties were (from strong to weak): <i>QN36, SCZ, ZC108, JX, JGY, XY10, QN1, MS9, QN38</i>, and <i>QN21</i>; (3) by using SVM, RF and PLSR to model DTC (drought tolerance coefficient) data, the best prediction model was selected as MSC-2D-UVE-SVM (R<sup>2</sup> = 0.77, RMSE = 0.073, MAPE = 0.16) for drought tolerance of tea germplasm resources, named Tea-DTC model. Therefore, the Tea-DTC model based on hyperspectral machine-learning technology can be used as a new screening method for evaluating tea germplasm resources with drought tolerance. Germplasm ; Hyperspectral imaging ; Drought tolerance ; Computer science ; Biology ; Artificial intelligence ; Botany Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Phytochemicals and Antioxidant Activities
Authors: Mowen Niu | Chongxiao Cui | Rui Tian | Yushun Zhao | Linlin Miao | Weizhe Hao | Jiaxuan Li | Chao Sui | Xiaodong He | Chao Wang
Source: RSC Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Mechanical and thermal properties of carbon nanotubes in carbon nanotube fibers under tension–torsion loading In carbon nanotube fibers (CNFs) fabricated by spinning methods, it is well-known that the mechanical and thermal performances of CNFs are highly dependent on the mechanical and thermal properties of the inherent CNTs. Furthermore, long CNTs are usually preferred to assemble CNFs because the interaction and entanglement between long CNTs are effectively stronger than between short CNTs. However, in CNFs fabricated using long CNTs, the interior carbon nanotubes (CNTs) inevitably undergo both tension and torsion loading when they are stretched, which would influence the mechanical and thermal performances of CNFs. Here, molecular dynamics (MD) simulations were carried out to study the mechanical and thermal properties of individual CNTs under tension-torsion loading. As for mechanical properties, it was found that both the fracture strength and Young's modulus of CNTs decreased as the twist angle <i>α</i> increased. Besides, step-wise fracture happened due to stress concentration when the twisted CNTs are stretched. On the other hand, it could be seen that the thermal conductivity of CNTs decreased as <i>α</i> increased. This work presents the systematic investigation of the mechanical and thermal properties of CNTs under tension-torsion loading and provides a theoretical guideline for the design and fabrication of CNFs. Torsion (gastropod) ; Carbon nanotube ; Materials science ; Thermal ; Composite material ; Tension (geology) ; Torsion spring ; Nanotechnology ; Thermodynamics ; Ultimate tensile strength ; Classical mechanics ; Physics Carbon Nanotubes in Composites ; Boron and Carbon Nanomaterials Research ; Fiber-reinforced polymer composites