jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Julia P. Schessner | Vincent Albrecht | Alexandra K. Davies | Pavel Sinitcyn | Georg H. H. Borner
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-11-09)
Field: Chemistry — Spectroscopy

Deep and fast label-free Dynamic Organellar Mapping ABSTRACT The Dynamic Organellar Maps (DOMs) approach combines cell fractionation and shotgun-proteomics for global profiling analysis of protein subcellular localization. Here, we have drastically enhanced the performance of DOMs through data-independent acquisition (DIA) mass spectrometry (MS). DIA-DOMs achieve twice the depth of our previous workflow in the same MS runtime, and substantially improve profiling precision and reproducibility. We leveraged this gain to establish flexible map formats scaling from rapid analyses to ultra-deep coverage. Our fastest format takes only ∼2.5h/map and enables high-throughput experimental designs. Furthermore, we introduce DOM-QC, an open-source software tool for in-depth standardized analysis of DOMs and other profiling data. We then applied DIA-DOMs to capture subcellular localization changes in response to starvation and disruption of lysosomal pH in HeLa cells, which revealed a subset of Golgi proteins that cycle through endosomes. DIA-DOMs offer a superior workflow for label-free spatial proteomics as a systematic phenotype discovery tool. Workflow ; Computer science ; Profiling (computer programming) ; Proteomics ; Golgi apparatus ; Proteome ; Software ; Computational biology ; Bioinformatics ; Chemistry ; Cell ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Sushardi Mastap | Tibertius Agus Prayitno | Yustinus Suranto | Ganis Lukmandaru
Source: Journal of Sylva Indonesiana (article, 2021-08-30)
Field: Chemistry — Organic Chemistry

Suitability of Teak Log Quality from Gunung Kidul and Bantul Yogyakarta Community Forest for Export Meubel Purpose Natural defect or poor log quality are common in community teak forests. Furniture manufacturers typically use these log types for export purposes. The aim of this study is to determine the effect of community teak forest location and teak stand age affect on teak wood quality. Three teak forest locations were Bantul and two locations in Gunung Kidul. The teak stand age class namely 6, 8, and 10 years old. The data was analyzed using SPSS 20.0 with Tukey test. The result showed that brittleness defect, sapwood defect, and different wooden knot defects were significant in all locations, while log straightness defect was found only in Dlingo. Similarly, all teak stand age (6, 8, and 10 years old) also produced the same defects such as brittleness defect, sapwood defect, and wood knot defect, while the straightness defect at the age of 10 years old was different from to other two teak stand age (6 and 8-year-old). Average teak log defects were straightness defect 1.87-3.53%, brittleness defect 1.19–6.21%, sapwood defect 1.49-4.82 cm, and wood knot defect 5.10-11.46 cm. However, the teak log quality still met the SNI 7534.2-2010 and 7535.2-2010 as raw material for exporting furniture. Forestry ; Mathematics ; Knot (papermaking) ; Pulp and paper industry ; Horticulture ; Environmental science ; Geography ; Engineering ; Biology Wood and Agarwood Research ; Agricultural and Environmental Management ; Wood Treatment and Properties
Authors: Víctor Pacheco‐Peña | Nader Engheta
Source: New Journal of Physics (preprint, 2021-08-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spatiotemporal isotropic-to-anisotropic meta-atoms Abstract Metamaterials and metasurfaces are designed by spatially arranging (periodically or non-periodically) subwavelength geometries, allowing a tailored manipulation of the electromagnetic response of matter. Here, we exploit temporal variations of permittivity inside subwavelength geometries to propose the concept of spatiotemporal meta-atoms having time-dependent properties. We exploit isotropic-to-anisotropic temporal boundaries within spatially subwavelength regions where their permittivity is rapidly changed in time. In so doing, it is shown how resulting scattered waves travel in directions that are different from the direction of the impinging wave, and depend on the values of the chosen anisotropic permittivity tensor. To provide a full physical insight of their performance, multiple scenarios are studied numerically such as the effect of using different values of permittivity tensor, different geometries of the spatiotemporal meta-atom and time duration of the induced isotropic-to-anisotropic temporal boundary. The intrinsic asymmetric response of the proposed spatiotemporal meta-atoms is also studied demonstrating, both theoretically and numerically, its potential for an at-will manipulation of scattered waves in real time. These results may open new paradigms for controlling wave–matter interactions and may pave the way for the next generation of metamaterials and metasurfaces by unleashing their potential using four-dimensional unit cells. Isotropy ; Metamaterial ; Anisotropy ; Permittivity ; Tensor (intrinsic definition) ; Physics ; Computational physics ; Optics ; Optoelectronics ; Dielectric ; Geometry ; Mathematics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Radio Wave Propagation Studies
Authors: Luciana Fontes de Oliveira | Cristina Braga | Fábio Augusto | Ronei J. Poppi
Source: Brazilian Journal of Analytical Chemistry (article, 2021-05-25)
Field: Chemistry — Spectroscopy

Correlating Comprehensive Two-dimensional Gas Chromatography Volatile Profiles of Chocolate with Sensory Analysis The identification of key components relevant to sensory perception of quality from commercial chocolate samples was accomplished after chemometric processing of GC×GC-MS (Comprehensive Two-dimensional Gas Chromatography with Mass Spectrometric Detection) profiles corresponding to HS-SPME (Headspace Solid Phase Microextraction) extracts of the samples. Descriptive sensory evaluation of samples was carried out using Optimized Descriptive Profile (ODP) procedures, where sensory attributes of 24 commercial chocolate samples were used to classify them in two classes (low and high chocolate flavor). 2D Fisher Ratio analysis was applied to four-way chromatographic data tensors (1st dimension retention time 1tR × 2nd dimension retention time 2tR × m/z × sample), to identify the crucial areas on the chromatograms that resulted on ODP class separation on Principal Component Analysis (PCA) scores plot. Comparing the relevant sections of the chromatograms to the analysis of the corresponding mass spectra, it was possible to assess that most of the information regarding the sample main sensory attributes can be related to only 14 compounds (2,5-dimethylpyrazine, 2,6-dimethyl-4-heptanol, 1-octen-3-ol, trimethylpyrazine, β-pinene, o-cimene, 2-ethyl-3,5-dimethylpyrazine, tetramethylpyrazine, benzaldehyde, 1,3,5-trimethylbenzene, 6-methyl-5-hepten-2-one, limonene, benzeneethanol and 1,1-dimethylbutylbenzene) among the complex blend of volatiles found on these extremely complex samples. Chromatography ; Chemistry ; Gas chromatography ; Solid-phase microextraction ; Principal component analysis ; Chemometrics ; Flavor ; Sensory analysis ; Electronic nose ; Limonene ; Gas chromatography–mass spectrometry ; Mass spectrometry ; Food science ; Mathematics ; Artificial intelligence ; Computer science ; Essential oil Analytical Chemistry and Chromatography ; Sensory Analysis and Statistical Methods ; Food Chemistry and Fat Analysis
Authors: Zhicai Yu | Yueqi Zhong | Haoyang Xie
Source: International Journal of Machine Learning and Computing (article, 2021-09-23)
Field: Materials Science — Polymers and Plastics

Reconstructing the Three-Dimensional Point Cloud of a Draped Fabric Based On a Two-Dimensional Projection In order to reconstruct the three-dimensional (3D) point cloud of a draped fabric based on a two-dimensional fabric drape projection, the three-dimensional point clouds of the draped fabrics were scanned with a self-built 3D scanning device. A resampling method based on local linear embedding (LLE) was used to represent different 3D point clouds with the same point number and point sequence. Principal Component Analysis (PCA) was used to reduce the dimension of the resampled 3D point clouds. With PCA, a completed resampled point cloud could be represented with a signature of length fifty-seven. At last, a regression model with a two-dimensional (2D) fabric drape projection as input was constructed and trained to predict the signature of length fifty-seven. With the predicted signature, the 3D point cloud of a draped fabric could be reconstructed. The result shows that all resampled 3D point clouds of draped fabric have the same point number and point sequence. The errors between the reconstructed 3D point clouds and the ground truth are all within 6.92 mm. Computer science ; Point cloud ; Projection (relational algebra) ; Point (geometry) ; Computer vision ; Artificial intelligence ; Computer graphics (images) ; Cloud computing ; Algorithm ; Geometry ; Mathematics Textile materials and evaluations ; Optical measurement and interference techniques ; Industrial Vision Systems and Defect Detection
Authors: A. Parvathy Eswari
Source: International Journal of Current Microbiology and Applied Sciences (article, 2021-04-10)
Field: Chemistry — Analytical Chemistry

Weather Based Yield Prediction and PDI Model for Grape Production Quality Forecast in Tamil Nadu using Mathematical Modelling In this paper, we developed the weather based yield prediction model between weather factors such as maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH1), (RH2) and rainfall (RF) observed at Grapes Research station, Theni district during the summer and winter season from 2016 to 2020. This model is built using multiple regression analysis to explain the relationship between grape yield as well as climatic parameters and quality assessment. Our findings indicate that the models provide an effective way to the evaluate and forecast. Furthermore, we show that there exits many factors like downy mildew and powdery mildew to affect the quality of grape, our yield prediction model provide superior performance for grape analysis. Furthermore, the PDI model was developed to forecasting disease incidence; yield of grape by using climatic scenario. A comparison of our estimated results with the numerical simulation and experimental result available is provided. Tamil ; Yield (engineering) ; Production (economics) ; Quality (philosophy) ; Environmental science ; Computer science ; Economics ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Horticultural and Viticultural Research
Authors: Shih-Hung Cheng | Chieh-Ju Lee
Source: International Journal of Affective Engineering (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Gender Differences in Footwear Visual Perception This study explores the differences in visual perception of footwear based on gender by conducting semantic differential evaluations and classification tasks. Cluster and factor analyses were performed to analyze the similarities and factors in footwear styles, respectively. The cluster analysis results indicated that leather shoes and sneakers were the major styles clearly distinguished by the participants. Fancy leather shoes, described as “fancy and exaggerated” by men, and as “elegant and refined” by women, was the consensus style. Women emphasized on fashion and were more likely to perceive footwear integral to their outfit, whereas men valued the functionality of footwear and treated it as a tool. The factor analysis on footwear styles extracted four male factors: evaluation, attention, feature, and protection; and four female factors for: evaluation, charm, volume, and coverage. Women were more sensitive to the volume and weight of footwear, and were also more likely to sense its coverage. Perception ; Psychology ; Style (visual arts) ; Semantic differential ; Cluster (spacecraft) ; Visual perception ; Social psychology ; Cognitive psychology ; Applied psychology ; Computer science ; Visual arts ; Art Textile materials and evaluations ; Color perception and design
Authors: Jinlei Hu | Yangyang Li | Junju Lai | Wei Wang | Fan Yang | Liu Yuhang | Zhao Yao
Source: Journal of Physics Conference Series (article, 2021-02-01)
Field: Materials Science — Materials Chemistry

Analysis of Condensation Problem in High Voltage Switchgear and Design of Anti-condensation System Abstract The southern region is hot, rainy and humid, and the switchgear is prone to moisture and condensation during long-term operation in this environment, causing partial discharge and other failures. To solve the problem that the switchgear is prone to condensation, this paper studies the conditions for the condensation phenomenon and analyses the condensation temperature calculation curve based on the air temperature and relative humidity in the cabinet. Then a three-dimensional model of the switchgear was established, and the thermal-electric coupling finite element simulation was performed to obtain the spatial temperature distribution of the switchgear under long-term operation and different humidity states. The simulation results are combined with the condensation temperature calculation curve to study the condensation situation of the switchgear under different conditions and the parts prone to condensation. Finally, according to the analysis results, a moisture-proof and anti-condensation control and early-warning system based on optical fiber temperature and humidity sensor is provided to detect the data in the cabinet and conduct comprehensive research and judgment, which provides theoretical basis for anti-condensation and intelligent operation and maintenance of switch cabinet. Switchgear ; Condensation ; Humidity ; Cabinet (room) ; Environmental science ; Meteorology ; Engineering ; Mechanical engineering ; Physics High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Aerosol Filtration and Electrostatic Precipitation
Authors: Fatemeh Pourmotahari | Seyyed Mohammad Tabatabaei | Nasrin Borumandnia | Naghmeh Khadembashi | Keyvan Olazadeh | Hamid Alavi Majd
Source: Basic and Clinical Neuroscience Journal (article, 2021-10-31)
Field: Chemistry — Analytical Chemistry

A Study Over Brain Connectivity Network of Parkinson's Patients, Using Nonparametric Bayesian Model The results of this study show a different topological configuration of the brain network between the patient and healthy groups, indicating changes in the functional relationship between a set of areas of the brain. Nonparametric statistics ; Bayesian probability ; Computer science ; Functional connectivity ; Bayesian network ; Artificial intelligence ; Psychology ; Machine learning ; Neuroscience ; Econometrics ; Mathematics Spectroscopy and Chemometric Analyses ; Functional Brain Connectivity Studies ; Traditional Chinese Medicine Studies
Authors: Bumju Kwak | Park, Jiwon | Kang, Taewon | Jeonghee Jo | Byunghan Lee | Sungroh Yoon
Source: arXiv (Cornell University) (preprint, 2021-06-29)
Field: Materials Science — Materials Chemistry

GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks. However, many existing models fail to fully consider the geometric information of molecular structures, resulting in less intuitive representations. Moreover, the widely used message-passing mechanism is limited to provide the interpretation of experimental results from a chemical perspective. To address these challenges, we introduce a novel Transformer-based framework for molecular representation learning, named the Geometry-aware Transformer (GeoT). GeoT learns molecular graph structures through attention-based mechanisms specifically designed to offer reliable interpretability, as well as molecular property prediction. Consequently, GeoT can generate attention maps of interatomic relationships associated with training objectives. In addition, GeoT demonstrates comparable performance to MPNN-based models while achieving reduced computational complexity. Our comprehensive experiments, including an empirical simulation, reveal that GeoT effectively learns the chemical insights into molecular structures, bridging the gap between artificial intelligence and molecular sciences. Interpretability ; Computer science ; Molecular graph ; Bridging (networking) ; Artificial intelligence ; Property (philosophy) ; Machine learning ; Theoretical computer science ; Graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Cristobal Colón-Ruíz
Source:  (preprint, 2021-12-08)
Field: Materials Science — Materials Chemistry

Semi-Supervised Generative Adversarial Network for Sentiment Analysis of drug reviews &lt;div&gt;Sentiment analysis has become a very popular research topic and covers a wide range of domains such as economy, politics and health. In the pharmaceutical field, automated analysis of online user reviews provides information on the effectiveness and potential side effects of drugs, which could be used to improve pharmacovigilance systems. Deep learning approaches have revolutionized the field of Natural Language Processing (NLP), achieving state-of-the-art results in many tasks, such as sentiment analysis.&lt;/div&gt;&lt;div&gt;These methods require large annotated datasets to train their models. However, in most real-world scenarios, obtaining high-quality labeled datasets is an expensive and time-consuming task. In contrast, unlabeled texts task can be, generally, easily obtained. &lt;/div&gt;&lt;div&gt;In this work, we propose a semi-supervised approach based on a Semi-Supervised Generative Adversarial Network (SSGAN) to address the lack of labeled data for the sentiment analysis of drug reviews, and improve the results provided by supervised approaches in this task.&lt;/div&gt;&lt;div&gt;To evaluate the real contribution of this approach, we present a benchmark comparison between our semi-supervised approach and a supervised approach, which uses a similar architecture but without the generative adversal setting. &lt;/div&gt;&lt;div&gt;Experimental results show better performance of the semi-supervised approach when annotated reviews are less than ten percent of the training set, obtaining a significant improvement for the classification of neutral reviews, the class with least examples. To the best of our knowledge, this is the first study that applies a SSGAN to the sentiment classification of drug reviews. Our semi-supervised approach provides promising results for dealing with the shortage of annotated dataset, but there is still much room to improvement.&lt;/div&gt; Computer science ; Benchmark (surveying) ; Sentiment analysis ; Artificial intelligence ; Task (project management) ; Machine learning ; Generative grammar ; Field (mathematics) ; Set (abstract data type) ; Deep learning ; Supervised learning ; Class (philosophy) ; Natural language processing ; Artificial neural network ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Kan Hatakeyama‐Sato | Kenichi Oyaizu
Source: arXiv (Cornell University) (preprint, 2021-02-27)
Field: Materials Science — Materials Chemistry

A Generative Model for Extrapolation Prediction in Materials Informatics We report a deep generative model for regression tasks in materials informatics. The model is introduced as a component of a data imputer, and predicts more than 20 diverse experimental properties of organic molecules. The imputer is designed to predict material properties by "imagining" the missing data in the database, enabling the use of incomplete material data. Even removing 60% of the data does not diminish the prediction accuracy in a model task. Moreover, the model excels at extrapolation prediction, where target values of the test data are out of the range of the training data. Such extrapolation has been regarded as an essential technique for exploring novel materials, but has hardly been studied to date due to its difficulty. We demonstrate that the prediction performance can be improved by &gt;30% by using the imputer compared with traditional linear regression and boosting models. The benefit becomes especially pronounced with few records for an experimental property (&lt; 100 cases) when prediction would be difficult by conventional methods. The presented approach can be used to more efficiently explore functional materials and break through previous performance limits. Extrapolation ; Computer science ; Boosting (machine learning) ; Generative grammar ; Materials informatics ; Regression ; Data mining ; Predictive modelling ; Machine learning ; Range (aeronautics) ; Property (philosophy) ; Artificial intelligence ; Generative model ; Linear regression ; Task (project management) ; Health informatics ; Mathematics ; Statistics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electron and X-Ray Spectroscopy Techniques
Authors: Samuel C. Hoffman | Vijil Chenthamarakshan | Dmitry Yu. Zubarev | Daniel P. Sanders | Payel Das
Source: arXiv (Cornell University) (preprint, 2021-12-02)
Field: Materials Science — Materials Chemistry

Sample-Efficient Generation of Novel Photo-acid Generator Molecules using a Deep Generative Model Photo-acid generators (PAGs) are compounds that release acids ($H^+$ ions) when exposed to light. These compounds are critical components of the photolithography processes that are used in the manufacture of semiconductor logic and memory chips. The exponential increase in the demand for semiconductors has highlighted the need for discovering novel photo-acid generators. While de novo molecule design using deep generative models has been widely employed for drug discovery and material design, its application to the creation of novel photo-acid generators poses several unique challenges, such as lack of property labels. In this paper, we highlight these challenges and propose a generative modeling approach that utilizes conditional generation from a pre-trained deep autoencoder and expert-in-the-loop techniques. The validity of the proposed approach was evaluated with the help of subject matter experts, indicating the promise of such an approach for applications beyond the creation of novel photo-acid generators. Autoencoder ; Computer science ; Generative grammar ; Generator (circuit theory) ; Deep learning ; Photolithography ; Artificial intelligence ; Generative model ; Machine learning ; Nanotechnology ; Materials science ; Physics Machine Learning in Materials Science ; Image Processing Techniques and Applications ; Software Testing and Debugging Techniques