jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Toman Borteçen | Torsten Müller | Jeroen Krijgsveld
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-03-03)
Field: Chemistry — Spectroscopy

An integrated workflow for quantitative analysis of the newly synthesized proteome Abstract The analysis of proteins that are newly synthesized upon a cellular perturbation can provide detailed insight in the proteomic response that is elicited by specific cues. This can be investigated by pulse-labeling of cells with clickable and stable-isotope-coded amino acids for enrichment and mass spectrometric characterization of newly synthesized proteins (NSPs), however convoluted protocols prohibit their routine application. Here we optimized multiple steps in sample preparation, mass spectrometry and data analysis, and integrated them in a semi-automated workflow for the quantitative analysis of the newly synthesized proteome (QuaNPA). Reduced input requirements and data-independent acquisition (DIA) enabled analysis of triple-SILAC-labeled NSP samples, with enhanced throughput while featuring high quantitative accuracy. We applied QuaNPA to investigate the time-resolved cellular response to interferon-gamma (IFNg), observing rapid induction of known and novel targets 2h after IFNg treatment. QuaNPA provides a powerful approach for large-scale investigation of NSPs to gain insight in complex cellular processes. Stable isotope labeling by amino acids in cell culture ; Proteome ; Workflow ; Quantitative proteomics ; Chemistry ; Mass spectrometry ; Proteomics ; Computer science ; Computational biology ; Chromatography ; Biochemistry ; Biology ; Database Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; RNA and protein synthesis mechanisms
Authors: Haomin Mao | Shuhei Tsuchida | Tsutomu Terada | Masahiko Tsukamoto
Source: Sensors (article, 2023-05-22)
Field: Materials Science — Polymers and Plastics

Estimating Scalp Moisture in a Hat Using Wearable Sensors Hair quality is easily affected by the scalp moisture content, and hair loss and dandruff will occur when the scalp surface becomes dry. Therefore, it is essential to monitor scalp moisture content constantly. In this study, we developed a hat-shaped device equipped with wearable sensors that can continuously collect scalp data in daily life for estimating scalp moisture with machine learning. We established four machine learning models, two based on learning with non-time-series data and two based on learning with time-series data collected by the hat-shaped device. Learning data were obtained in a specially designed space with a controlled environmental temperature and humidity. The inter-subject evaluation showed a Mean Absolute Error (MAE) of 8.50 using Support Vector Machine (SVM) with 5-fold cross-validation with 15 subjects. Moreover, the intra-subject evaluation showed an average MAE of 3.29 in all subjects using Random Forest (RF). The achievement of this study is using a hat-shaped device with cheap wearable sensors attached to estimate scalp moisture content, which avoids the purchase of a high-priced moisture meter or a professional scalp analyzer for individuals. Wearable computer ; Scalp ; Moisture ; Computer science ; Support vector machine ; Water content ; Dandruff ; Inkwell ; Artificial intelligence ; Random forest ; Simulation ; Machine learning ; Environmental science ; Engineering ; Medicine ; Speech recognition ; Geography ; Embedded system ; Meteorology ; Surgery ; Geotechnical engineering Textile materials and evaluations ; Animal Behavior and Welfare Studies ; Skin Protection and Aging
Authors: Yuanyuan Han | Yuanming Zhang | Haiguang Zhao | H Li | Z Kang | L Yang | S Lee | K Naik | S Chaudhary | L Ye | A Parmar | K Lu | Q Quan | N Zhang | Y Xu | R Wang | Z Tang | X Wang | M Wang | G Liu | Y Zhang | G Han | A Vomiero | H Zhao | V Parthiban | S Panda | A Sahu | H Wang | P Sun | S Cong | J Wu | L Gao | Y Wang | X Dai | Q Yi | G Zou | K Wang | X Tong | Y Zhou | H Zhang | F Navarro-Pardo | G Selopal | J Tang | S Sun | D Ma | Z Wang | F Vidal | X Sun | F Rosei | S You | F Camargo | M Zavelani-Rossi | C Sun | B Liu | X Gong | L Zhang | Y Han | J Yang | S Deng | B Wang | X Liu | Z Yang | Y Yang | A Kaur | K Pandey | R Kaur | N Vashishat | M Kaur | J Guo | L Ling | G Li | R Cheng | X Lu | A Xie | Q Li | C Wang | S Chen | S Li | M Qi | Y Fan | M Anpo | Y Yamada | B Weng
Source: General Chemistry (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Colloidal Carbon Quantum Dots for Anticounterfeiting More attentions have been paid for developing anti-counterfeiting technologies to fight the endless emergence of fake and inferior products. Compared with inorganic quantum dots, colloidal carbon dots (C-dots) are emerging fluorescent nanomaterials, which have been used for various types of optical and electrical applications, because they have composition-dependent optical properties, including high quantum yield, tunable absorption/emission spectra and good chemical-/photo-stability. In this minireview, we summarized and updated the most recent research works for the use of colloidal C-dots for optical anticounterfeiting applications. We focus on the fluorescent, persistent luminescence and phosphorescent C-dots. In the end, we enclosed this minireview with our owner perspectives on the challenges of C-dots based anticounterfeiting. Quantum dot ; Carbon quantum dots ; Colloid ; Carbon fibers ; Nanotechnology ; Materials science ; Chemical engineering ; Engineering ; Composite material ; Composite number Carbon and Quantum Dots Applications ; Molecular Communication and Nanonetworks
Authors: Huan Fang | Hai‐Long Wu | Tong Wang | Yao Chen | Ru‐Qin Yu
Source: Chemosensors (article, 2023-08-01)
Field: Chemistry — Analytical Chemistry

Fluorophores-Assisted Excitation Emission Matrix Fluorescence Method for the Origin Traceability of Lily In this work, a fluorophores-assisted excitation/emission matrix (EEM) fluorescence method was proposed to trace the origin of lily in the Chinese market. There are few active components in lilies that have fluorescent signals, and too few characteristic variables may lead to unsatisfactory accuracy in the subsequent classification. Therefore, three fluorophores, 2-Aminoethyl diphenylborinate (DPBA), o-Phthalaldehyde (OPA) and Rhodamine B (RB), were used to enrich the information of the fluorescent fingerprint of lily, which can improve the classification accuracy. The lily samples were characterized by using EEM fluorescence coupled with the alternating trilinear decomposition (ATLD) algorithm, which was able to extract information of various fluorophores in lily samples. Two chemical pattern recognition methods, principal component analysis-linear discriminant analysis (PCA-LDA) and partial least squares-discrimination analysis (PLS-DA), were used to model and trace the origin of different lilies. When the fluorophores were added, the accuracy of the test set and prediction set obtained by the classification model increased from 71.4% to 92.9% and 66.7% to 100%, respectively. The proposed method combined fluorophores-assisted EEM fluorescence with multi-way chemometric methods to extract comprehensive information on the samples, which provided a potential method for the origin traceability of traditional Chinese medicine. Fluorescence ; Traceability ; Chemistry ; Excitation wavelength ; Principal component analysis ; Biological system ; Partial least squares regression ; Matrix (chemical analysis) ; Rhodamine ; Analytical Chemistry (journal) ; Pattern recognition (psychology) ; Artificial intelligence ; Chromatography ; Computer science ; Machine learning ; Optics ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Traditional Chinese Medicine Analysis
Authors: Le Zhang | Fengjie Xi | Qiang Liu | Xiaojun Xu
Source: Microwave and Optical Technology Letters (article, 2023-10-25)
Field: Chemistry — Spectroscopy

Atmospheric transmission calculation of diode‐pumped alkali laser energy transfer Abstract Wireless power transfer has become a research hotspot in the field of energy transmission, among which laser energy transfer is one of the best approaches for long‐distance wireless transmission. Based on good atmospheric transmittance, especially the wavelengths consistent with the response range of mainstream Si and GaAs photovoltaic cells with relatively high photoelectric efficiency, diode‐pumped alkali laser (DPAL) energy transfer will have huge prospects. To solve the problem of long‐distance laser energy transfer of DPALs, laser atmospheric molecular absorption was calculated by analyzing the atmospheric transmission absorption effects of laser beams with specific wavelengths. The atmospheric transmission of three typical alkalis at different atmospheric modes and altitudes was calculated and compared, and practical suggestions were given. Laser ; Materials science ; Diode ; Optoelectronics ; Transmittance ; Photoelectric effect ; Transmission (telecommunications) ; Optics ; Absorption (acoustics) ; Physics ; Electrical engineering ; Engineering Spectroscopy and Laser Applications ; Random lasers and scattering media ; Quantum optics and atomic interactions
Authors: Michiaki Sakamoto | Itsuo Hanasaki
Source: Physical review. E (article, 2023-01-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Analytical model of critical buckling transition for smectic liquid crystal based on the viscoelastic scaling of coarse-grained molecular dynamics The buckling transition of smectic liquid crystals (LCs) is important not only as fundamental physics but also for the rational design of devices to make use of their optical and mechanical properties. However, there exists a huge gap between the specific knowledge and universal analytical formulation. We have conducted coarse-grained molecular dynamics (CGMD) simulations with the force field optimized for the description of buckling phenomena including topological defects to link the molecular nature and continuum formulation. The simulations reveal the viscoelastic characteristics where the critical strain and the compression modulus highly depend on the strain rate as well as the number of layers. Therefore, we formulate the scaling model whose coupling constants depend on both strain rate and domain size. The model reproduces the CGMD results as well as experimental and theoretical values in existing literature. Furthermore, we elucidate from this model that the critical buckling behavior is determined by the competition between the suppression of compression-induced flow and the undulation fluctuation of layers. The framework consisting of the CGMD simulation and the scaling model enables us to estimate the buckling characteristics of smectic LCs reflecting their molecular structures in a wide range from the low-frequency regime that can be verified by experiments to the high-frequency regime beyond the reach of it. Scaling ; Buckling ; Molecular dynamics ; Materials science ; Viscoelasticity ; Liquid crystal ; Modulus ; Statistical physics ; Strain rate ; Mechanics ; Chemical physics ; Computational chemistry ; Physics ; Composite material ; Chemistry ; Geometry ; Mathematics Liquid Crystal Research Advancements ; Nonlinear Dynamics and Pattern Formation ; Advanced Materials and Mechanics
Authors: Xiaoyi Ma | Yufeng Luo | Mengke Li | Wenyan Jiao | Hongmei Yuan | Huijun Liu | Ying Fang
Source: Chinese Physics B (article, 2023-01-13)
Field: Materials Science — Materials Chemistry

Machine learning of the Γ-point gap and flat bands of twisted bilayer graphene at arbitrary angles The novel electronic properties of bilayer graphene can be fine-tuned via twisting, which may induce flat bands around the Fermi level with nontrivial topology. In general, the band structure of such twisted bilayer graphene (TBG) can be theoretically obtained by using first-principles calculations, tight-binding method, or continuum model, which are either computationally demanding or parameters dependent. In this work, by using the sure independence screening sparsifying operator method, we propose a physically interpretable three-dimensional (3D) descriptor which can be utilized to readily obtain the Γ -point gap of TBG at arbitrary twist angles and different interlayer spacings. The strong predictive power of the descriptor is demonstrated by a high Pearson coefficient of 99% for both the training and testing data. To go further, we adopt the neural network algorithm to accurately probe the flat bands of TBG at various twist angles, which can accelerate the study of strong correlation physics associated with such a fundamental characteristic, especially for those systems with a larger number of atoms in the unit cell. Bilayer graphene ; Twist ; Physics ; Graphene ; Tight binding ; Topology (electrical circuits) ; Point (geometry) ; Fermi level ; Artificial neural network ; Condensed matter physics ; Electronic structure ; Computer science ; Quantum mechanics ; Geometry ; Mathematics ; Artificial intelligence ; Electron Graphene research and applications ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Роман Олексенко
Source: International Journal of Mathematics and Physics (article, 2023-12-01)
Field: Chemistry — Spectroscopy

SIMULATION PHOTOCONVERTERS OF porous-Si/Si WITH DIFFERENT ANTI-REFLECTIVE COATINGS Целью данной работы является исследование перспективных фоточувствительных структур солнечной энергетики на основе гетероструктуры пористый Si/Si с просветляющими покрытиями и без них. Для моделирования фотоконвертированных параметров использовалась программа PC1D. Изучены спектры отражения кремниевых солнечных элементов без покрытия и с просветляющими покрытиями SiO 2 , TiO 2 , ZnO, ZnS. Установлено, что коэффициент отражения чистой гетероструктуры пористый Si/Si на длине волны около 500 нм достигает 37 %, тогда как коэффициент отражения от АРО составляет не более 13 % для той же длины волны в случае падения света на 30°, а при угле падения солнечного света 45° значение коэффициента отражения составляет ⁓31% для пористого Si/Si и не превышает 16% для солнечных элементов с просветляющими покрытиями на длине волны около 500 нм. На основе исследованных структур были рассчитаны характеристики фотопреобразователя света, а также построены вольт-амперные характеристики. Первоначальные результаты моделирования показывают, что эффективность солнечных элементов на основе пористого Si/Si составляет около 17,5%. Значительного повышения эффективности солнечных батарей удалось достичь благодаря использованию антибликового покрытия. Среди четырех материалов просветляющее покрытие TiO 2 имеет наибольшее значение эффективности, которое достигло 26,4%. Изучено влияние текстурирования фронтальной поверхности на эффективность солнечного элемента. В отсутствие текстурирования эффективность солнечного элемента пористого Si/Si составила 17,1%, а с текстурированием достигла значения 18,1%. Для конструкций с просветляющим покрытием эффективность составляет 17,3% (SiO2 ) , 25,4% (TiO2 ) , 19,8% (ZnO), 19,8% (ZnS) без текстурирования поверхности и 18,3% (SiO2 ) , 26,4% ( TiO 2 ), 20,7 % (ZnO), 20,9 % (ZnS) с текстурированием. Рассмотрено влияние рабочей температуры на электрические характеристики солнечного элемента. Установлено, что повышение температуры приводит к снижению эффективности исследованных структур на: 2,5 % (без просветляющих покрытий), 2,3 % (SiO 2 ), 1,8 % (TiO 2 ), 2,4 % ( ZnO ) . , 2,9% (ZnS). Materials science ; Porosity ; Chemical engineering ; Silicon ; Nanotechnology ; Optoelectronics ; Composite material Aerogels and thermal insulation
Authors: Siamak Mirfendereski | Jae Sung Park
Source: Journal of Fluid Mechanics (article, 2023-12-19)
Field: Chemistry — Physical and Theoretical Chemistry

Rheology of dense suspensions of ideally conductive particles in an electric field The rheological behaviour of dense suspensions of ideally conductive particles in the presence of both electric field and shear flow is studied using large-scale numerical simulations. Under the action of an electric field, these particles are known to undergo dipolophoresis (DIP), which is the combination of two nonlinear electrokinetic phenomena: induced-charge electrophoresis (ICEP) and dielectrophoresis (DEP). For ideally conductive particles, ICEP is predominant over DEP, resulting in transient pairing dynamics. The shear viscosity and first and second normal stress differences $N_1$ and $N_2$ of such suspensions are examined over a range of volume fractions $15\,\% \leq \phi \leq 50\,\%$ as a function of Mason number $Mn$ , which measures the relative importance of viscous shear stress over electrokinetic-driven stress. For $Mn &lt; 1$ or low shear rates, the DIP is shown to dominate the dynamics, resulting in a relatively low-viscosity state. The positive $N_1$ and negative $N_2$ are observed at $\phi &lt; 30\,\%$ , which is similar to Brownian suspensions, while their signs are reversed at $\phi \ge 30\,\%$ . For $Mn \ge 1$ , the shear thickening starts to arise at $\phi \ge 30\,\%$ , and an almost five-fold increase in viscosity occurs at $\phi = 50\,\%$ . Both $N_1$ and $N_2$ are negative for $Mn \gg 1$ at all volume fractions considered. We illuminate the transition in rheological behaviours from DIP to shear dominance around $Mn = 1$ in connection to suspension microstructure and dynamics. Lastly, our findings reveal the potential use of nonlinear electrokinetics as a means of active rheology control for such suspensions. Electrokinetic phenomena ; Rheology ; Electric field ; Materials science ; Viscosity ; Shear stress ; Shear rate ; Dilatant ; Suspension (topology) ; Condensed matter physics ; Physics ; Nanotechnology ; Composite material Electrostatics and Colloid Interactions ; Vibration Control and Rheological Fluids ; Microfluidic and Bio-sensing Technologies
Authors: Bao-Wei Zhang Bao-Wei Zhang | Lin Zhang | Yong-Hua Wang Lin Xu
Source: 網際網路技術學刊 (article, 2023-05-01)
Field: Materials Science — Polymers and Plastics

Yarn Unevenness Prediction using Generalized Regression Neural Network &lt;p&gt;This study aimed to propose a method to predict yarn unevenness grounded on the generalized regression neural network and traditional neural network model to further improve the prediction accuracy. The yarn unevenness model was constructed. Under this model, a three-layer neural network, a four-layer neural network, a five-layer neural network, and a generalized regression neural network were designed. Finally, Python was used for training and simulation. The training parameters and the three network models data were made consistent to ensure the comparability of the results. The results showed that using the yarn unevenness model, the average relative error of the four-layer neural network to cut down 0.87% compared with that of the three-layer neural network. Compared with the five-layer neural network, the four-layer neural network performance was not much different, but the running speed was increased by 46.05%. Compared with the four-layer neural network, the average relative error of the generalized regression neural network was reduced by 0.57%, the mean square error was reduced by 0.98%, he root mean square error was reduced by 4.76%, and the running speed was increased by 74.70%.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; Artificial neural network ; Mean squared error ; Computer science ; Regression ; Approximation error ; Layer (electronics) ; Time delay neural network ; Regression analysis ; Artificial intelligence ; Feedforward neural network ; Probabilistic neural network ; Algorithm ; Statistics ; Machine learning ; Mathematics Textile materials and evaluations
Authors: Hermanni Hälvä | Jonathan So | Richard E. Turner | Aapo Hyvärinen
Source: arXiv (Cornell University) (preprint, 2023-11-28)
Field: Chemistry — Analytical Chemistry

Identifiable Feature Learning for Spatial Data with Nonlinear ICA Recently, nonlinear ICA has surfaced as a popular alternative to the many heuristic models used in deep representation learning and disentanglement. An advantage of nonlinear ICA is that a sophisticated identifiability theory has been developed; in particular, it has been proven that the original components can be recovered under sufficiently strong latent dependencies. Despite this general theory, practical nonlinear ICA algorithms have so far been mainly limited to data with one-dimensional latent dependencies, especially time-series data. In this paper, we introduce a new nonlinear ICA framework that employs $t$-process (TP) latent components which apply naturally to data with higher-dimensional dependency structures, such as spatial and spatio-temporal data. In particular, we develop a new learning and inference algorithm that extends variational inference methods to handle the combination of a deep neural network mixing function with the TP prior, and employs the method of inducing points for computational efficacy. On the theoretical side, we show that such TP independent components are identifiable under very general conditions. Further, Gaussian Process (GP) nonlinear ICA is established as a limit of the TP Nonlinear ICA model, and we prove that the identifiability of the latent components at this GP limit is more restricted. Namely, those components are identifiable if and only if they have distinctly different covariance kernels. Our algorithm and identifiability theorems are explored on simulated spatial data and real world spatio-temporal data. Identifiability ; Independent component analysis ; Nonlinear system ; Inference ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Gaussian process ; Covariance ; Heuristic ; Algorithm ; Artificial neural network ; Machine learning ; Mathematics ; Gaussian ; Statistics Spectroscopy and Chemometric Analyses ; Blind Source Separation Techniques ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ernesto Paiva-Peredo | Diego Gonzales-Rodriguez | W. T. Herrera | Juan J. Soria | Diana Quispe-Arpasi | Christian Ovalle
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2023-11-14)
Field: Chemistry — Analytical Chemistry

Transformations for non-destructive evaluation of brix in mango by reflectance spectroscopy and machine learning Mango is a very popular climacteric fruit in America and Europe. Among the internal properties of the mango, total soluble solids (TSS) are an adequate indicator to estimate the quality of mango, however, the measurement of this indicator requires destructive tests. Several research have addressed similar issues; they have made use of pre-processing transformations without making it clear which of them is statistically better. Here, we created a new spectral database to build machine learning (ML) models. We analyzed a total of 18 principal component regression (PCR) models and 18 partial least squared regression (PLSR) models, where 4 types of transformations, 3 different feature extractors, and 3 different pre-processing techniques are combined. The research proposes a double cross validation (CV) both to determine the optimal number of components and to obtain the final metrics. The best model had a root mean square error (RMSE) of 1.1382 °Brix and a RMSE on the transformed scale of 0.5140. The best model used 4 components, used y&lt;sup&gt;2&lt;/sup&gt; transformation, reflectance R as the independent variable and MSC as a pre-processing technique. Mean squared error ; Principal component analysis ; Partial least squares regression ; Mathematics ; Feature (linguistics) ; Transformation (genetics) ; Artificial intelligence ; Climacteric ; Regression analysis ; Regression ; Pattern recognition (psychology) ; Statistics ; Linear regression ; Machine learning ; Computer science ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Phytochemicals and Antioxidant Activities