jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qingzu He | Chuan‐Qi Zhong | Xiang Li | Huan Guo | Yiming Li | Mingxuan Gao | Rongshan Yu | Xianming Liu | Fangfei Zhang | Donghui Guo | Fangfu Ye | Tiannan Guo | Jianwei Shuai | Jiahuai Han
Source: Research (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Dear-DIA <sup>XMBD</sup> : Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIA<sup>XMBD</sup>, for direct analysis of DIA data. Dear-DIA<sup>XMBD</sup> first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the <i>k</i>-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIA<sup>XMBD</sup> performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIA<sup>XMBD</sup> is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD. Autoencoder ; Computer science ; Cluster analysis ; Data acquisition ; Deconvolution ; Mass spectrometry ; Fragment (logic) ; Artificial intelligence ; Pattern recognition (psychology) ; Chemistry ; Data mining ; Algorithm ; Artificial neural network ; Chromatography ; Programming language Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Magdalena Cifuentes-Cabezas | Alessio Pavani | María‐Cinta Vincent‐Vela | J.A. Mendoza‐Roca | Silvia Álvarez‐Blanco
Source: Environmental Technology & Innovation (article, 2023-02-10)
Field: Chemistry — Organic Chemistry

Concentration of phenolic compounds from olive washing wastewater by forward osmosis using table olive fermentation brine as draw solution The olive industry has a considerable amount of process water consumption. As a result of the process, a large volume of oil mill wastewater (OMW) and table olive wastewater (TOW), both of them with high concentrations of organic matter, phenolic compounds and conductivity, are released. The appropriate management of these wastewaters is of paramount importance due to their toxic impacts on the environment. In this work, forward osmosis (FO) process is presented as an option to concentrate phenolic compounds for their further recovery. Two membranes, OsmoF2OTM (FTS, USA) and HFFO.6 (Aquaporin, Denmark) were evaluated with NaCl as draw solution (DS) and olive oil washing wastewater (OOWW) filtered at 5μm (5F) as feed solution (FS). Both membranes were able to concentrate the phenolic compounds with acceptable volume reduction (VR) values. The HFFO.6 membrane, which presented the lowest passage of phenolic compounds (TPhC) to the DS, was tested with fermentation brine from table olive processing (FTOP) as DS. As FS the same 5F-OOWW as well as an OOWW ultrafiltration permeate (UF-OOWW) were considered. Interestingly, greater percentages of TPhC concentration were observed compared to the tests with NaCl as DS (74.13–76.93% versus 62.6%, respectively). Although the tests with UF-OOWW presented greater flux, the recovery percentages of TPhC were similar, even slightly lower than those obtained with 5F-OOWW. Therefore, FO appears as a promising option to concentrate TPhC from OOWW and as well as to dilute FTOP. Thus both wastewaters from the olive industry could be treated at the same time. Wastewater ; Brine ; Chemistry ; Pulp and paper industry ; Olive oil ; Forward osmosis ; Fermentation ; Chromatography ; Reverse osmosis ; Ultrafiltration (renal) ; Effluent ; Membrane ; Osmosis ; Food science ; Environmental science ; Environmental engineering ; Organic chemistry ; Biochemistry Edible Oils Quality and Analysis ; Membrane Separation Technologies ; Electrochemical sensors and biosensors
Authors: Mohammad Hajeb | Saeid Hamzeh | Seyed Kazem Alavipanah | Lamya Neissi | Jochem Verrelst
Source: International Journal of Applied Earth Observation and Geoinformation (article, 2023-01-03)
Field: Chemistry — Analytical Chemistry

Simultaneous retrieval of sugarcane variables from Sentinel-2 data using Bayesian regularized neural network Quantifying biophysical and biochemical vegetation variables is of great importance in precision agriculture. Here, the ability of artificial neural networks (ANNs) to generate multiple outputs is exploited to simultaneously retrieve Leaf area index (LAI), leaf sheath moisture (LSM), leaf chlorophyll content (LCC), and leaf nitrogen concentration (LNC) of sugarcane from Sentinel-2 spectra. We apply a type of ANNs, Bayesian Regularized ANN (BRANN), which incorporates the Bayes' theorem into a regularization scheme to tackle the overfitting problem of ANN and improve its generalizability. Quantitatively assessing the result accuracy indicated RMSE values of 0.48 (m<sup>2</sup>/m<sup>2</sup>) for LAI, 2.36 (% wb) for LSM, 5.85 (μg/cm<sup>2</sup>) for LCC, and 0.23 (%) for LNC, applying simultaneous retrieval. It was demonstrated that simultaneous retrievals of the variables outperformed the individual retrievals. The superiority of the proposed BRANN over a conventional ANN trained with the Levenberg-Marquardt algorithm was confirmed through statistical comparison of their results. The model was applied over the entire Sentinel-2 images to map the considered variables. The maps were probed to qualitatively evaluate the model performance. The results indicated that the retrievals reasonably represent spatial and temporal variations of the variables. Generally, this study demonstrated that the BRANN simultaneous retrieval model can provide faster and more accurate retrievals than those obtained from conventional ANNs and individual retrievals. Artificial neural network ; Bayesian probability ; Geography ; Deep neural networks ; Bayesian network ; Data mining ; Artificial intelligence ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Sugarcane Cultivation and Processing ; Remote Sensing in Agriculture
Authors: Pietro Patimisco | Nicoletta Ardito | Edoardo De Toma | Dominik Burghart | Vladislav Tigaev | Mikhail A. Belkin | Vincenzo Spagnolo
Source: Sensors (article, 2023-11-06)
Field: Chemistry — Spectroscopy

Quartz-Enhanced Photoacoustic Sensor Based on a Multi-Laser Source for In-Sequence Detection of NO2, SO2, and NH3 In this work, we report on the implementation of a multi-quantum cascade laser (QCL) module as an innovative light source for quartz-enhanced photoacoustic spectroscopy (QEPAS) sensing. The source is composed of three different QCLs coupled with a dichroitic beam combiner module that provides an overlapping collimated beam output for all three QCLs. The 3λ-QCL QEPAS sensor was tested for detection of NO<sub>2</sub>, SO<sub>2</sub>, and NH<sub>3</sub> in sequence in a laboratory environment. Sensitivities of 19.99 mV/ppm, 19.39 mV/ppm, and 73.99 mV/ppm were reached for NO<sub>2</sub>, SO<sub>2</sub>, and NH<sub>3</sub> gas detection, respectively, with ultimate detection limits of 9 ppb, 9.3 ppb, and 2.4 ppb for these three gases, respectively, at an integration time of 100 ms. The detection limits were well below the values of typical natural abundance of NO<sub>2</sub>, SO<sub>2</sub>, and NH<sub>3</sub> in air. Quantum cascade laser ; Photoacoustic spectroscopy ; Collimated light ; Beam (structure) ; Laser ; Detection limit ; Parts-per notation ; Photoacoustic imaging in biomedicine ; Quartz ; Cascade ; Materials science ; Optoelectronics ; Optics ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Chromatography Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Atmospheric and Environmental Gas Dynamics
Authors: Ruben Halfwerk | Doekle Yntema | Jaap van Spronsen | Karel J. Keesman | Albert van der Padt
Source: Journal of Food Engineering (article, 2023-01-06)
Field: Materials Science — Materials Chemistry

Crystallization kinetics of lactose recovered at sub-zero temperatures: A population balance model combining mutarotation, nucleation and crystal growth Crystallization kinetics of lactose at sub-zero temperatures are modelled in this paper using a population balance model. There are two competing effects on the crystallization kinetics at low temperatures. On the one hand, mass transport and the rate of both mutarotation and nucleation are reduced, reducing the crystallization rate. On the other hand, supersaturation is increased, which increases nucleation and crystal growth and thereby increases the crystallization rate. To explore this phenomenon, the crystal growth of α-lactose monohydrate at sub-zero conditions was modelled, including the effects of mutarotation and nucleation. The model is compared with experiments. It was found that stirring did not have a significant influence, indicating that the process was not limited by mass transfer. Mutarotation had a significant effect; the model showed fast depletion of α-lactose; at high lactose concentrations, the mutarotation of β-lactose could not keep up with this depletion. Mutarotation ; Nucleation ; Crystallization ; Supersaturation ; Lactose ; Chemistry ; Kinetics ; Crystal growth ; Crystal (programming language) ; Thermodynamics ; Population ; Crystallography ; Physics ; Stereochemistry ; Organic chemistry Crystallization and Solubility Studies ; Freezing and Crystallization Processes ; Microencapsulation and Drying Processes
Authors: Deema Moharram | Xuguang Yuan | Dan Li
Source: Applied Sciences (article, 2023-01-09)
Field: Chemistry — Organic Chemistry

Tree Seedlings Detection and Counting Using a Deep Learning Algorithm Tree-counting methods based on computer vision technologies are low-cost and efficient in contrast to the traditional tree counting methods, which are time-consuming, laborious, and humanly infeasible. This study presents a method for detecting and counting tree seedlings in images using a deep learning algorithm with a high economic value and broad application prospects in detecting the type and quantity of tree seedlings. The dataset was built with three types of tree seedlings: dragon spruce, black chokeberries, and Scots pine. The data were augmented via several data augmentation methods to improve the accuracy of the detection model and prevent overfitting. Then a YOLOv5 object detection network was built and trained with three types of tree seedlings to obtain the training weights. The results of the experiments showed that our proposed method could effectively identify and count the tree seedlings in an image. Specifically, the MAP of the dragon spruce, black chokeberries, and Scots pine tree seedlings were 89.8%, 89.1%, and 95.6%, respectively. The accuracy of the detection model reached 95.10% on average (98.58% for dragon spruce, 91.62% for black chokeberries, and 95.11% for Scots pine). The proposed method can provide technical support for the statistical tasks of counting trees. Scots pine ; Tree (set theory) ; Overfitting ; Black spruce ; Artificial intelligence ; Computer science ; Mathematics ; Pattern recognition (psychology) ; Algorithm ; Pinus <genus> ; Forestry ; Botany ; Taiga ; Geography ; Artificial neural network ; Biology Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Smart Agriculture and AI
Authors: Alen Rončević | Nenad Koruga | Anamarija Soldo Koruga | Željko Debeljak | Robert Rončević | Tajana Turk | Domagoj Kretić | Tatjana Rotim | Zdravka Krivdić Dupan | Damir Troha | Marija Perić | Tihana Šimundić
Source: Current Issues in Molecular Biology (review, 2023-01-18)
Field: Chemistry — Spectroscopy

MALDI Imaging Mass Spectrometry of High-Grade Gliomas: A Review of Recent Progress and Future Perspective Glioblastoma (GBM) is the most common malignancy of the brain with a relatively short median survival and high mortality. Advanced age, high socioeconomic status, exposure to ionizing radiation, and other factors have been correlated with an increased incidence of GBM, while female sex hormones, history of allergies, and frequent use of specific drugs might exert protective effects against this disease. However, none of these explain the pathogenesis of GBM. The most recent WHO classification of CNS tumors classifies neoplasms based on their histopathological and molecular characteristics. Modern laboratory techniques, such as matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry, enable the comprehensive metabolic analysis of the tissue sample. MALDI imaging is able to characterize the spatial distribution of a wide array of biomolecules in a sample, in combination with histological features, without sacrificing the tissue integrity. In this review, we first provide an overview of GBM epidemiology, risk, and protective factors, as well as the recent WHO classification of CNS tumors. We then provide an overview of mass spectrometry workflow, with a focus on MALDI imaging, and recent advances in cancer research. Finally, we conclude the review with studies of GBM that utilized MALDI imaging and offer our perspective on future research. Mass spectrometry imaging ; Medicine ; MALDI imaging ; Malignancy ; Disease ; Pathology ; Glioma ; Oncology ; Computational biology ; Bioinformatics ; Matrix-assisted laser desorption/ionization ; Mass spectrometry ; Biology ; Cancer research ; Chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Machine Learning in Materials Science
Authors: Mary Etta King | Monica Lin | Meredith Spradlin | Lívia S. Eberlin
Source: Annual Review of Analytical Chemistry (review, 2023-03-21)
Field: Chemistry — Spectroscopy

Advances and Emerging Medical Applications of Direct Mass Spectrometry Technologies for Tissue Analysis Offering superb speed, chemical specificity, and analytical sensitivity, direct mass spectrometry (MS) technologies are highly amenable for the molecular analysis of complex tissues to aid in disease characterization and help identify new diagnostic, prognostic, and predictive markers. By enabling detection of clinically actionable molecular profiles from tissues and cells, direct MS technologies have the potential to guide treatment decisions and transform sample analysis within clinical workflows. In this review, we highlight recent health-related developments and applications of direct MS technologies that exhibit tangible potential to accelerate clinical research and disease diagnosis, including oncological and neurodegenerative diseases and microbial infections. We focus primarily on applications that employ direct MS technologies for tissue analysis, including MS imaging technologies to map spatial distributions of molecules in situ as well as handheld devices for rapid in vivo and ex vivo tissue analysis. Workflow ; Characterization (materials science) ; Computer science ; Mass spectrometry imaging ; Computational biology ; Nanotechnology ; Mass spectrometry ; Data science ; Materials science ; Chemistry ; Biology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Junming Hou | Zhenhu HE | Deyu LIU | Ziang Zhu | Zhenghang Long | Yue Xiang | Wei Wang
Source: Food Science and Technology (review, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Mechanical damage characteristics and nondestructive testing techniques of fruits: a review Fruits will be subjected inevitably to various external forces in the process of harvesting, transportation, processing, and storage, which will cause mechanical damage. The research on mechanical properties and damage mechanisms of fruit can effectively control its loss. In this study, fruits are divided into different types according to their morphology and structure. The impact, vibration, static pressure, and other mechanical damage on fruits are studied. It is important to identify the damaged parts of fruit after damage quickly and accurately. Therefore, this study analyzes the application of nondestructive testing technologies such as spectral detection technology, NMR (nuclear magnetic resonance) detection technology, and acoustic and electrical characteristics detection technology in fruit damage detection. Nondestructive testing ; Materials science ; Vibration ; Process (computing) ; Computer science ; Forensic engineering ; Environmental science ; Acoustics ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Meat and Animal Product Quality
Authors: Angie L. Gámez | Thomas Vatter | L.G. Santesteban | J. L. Araus | Íker Aranjuelo
Source: Computers and Electronics in Agriculture (article, 2023-12-12)
Field: Chemistry — Analytical Chemistry

Onfield estimation of quality parameters in alfalfa through hyperspectral spectrometer data Alfalfa is a forage of vast importance around the world. In the past, near-infrared spectroscopy (NIRS) technique have been explored in the lab to determine quality traits such as fibre content in dried and ground material. During the last decade, portable hyperspectral devices have emerged as a tools for in-field prediction, of not only crop yield but also a large range of quality and physiological markers. The objective of this study was to estimate quality parameters in an alfalfa crop using hyperspectral data acquired from a full-range (350–2500 nm) spectrometer under field conditions. Reflected spectra were measured in single leaves as well as at the canopy level, then reflectance was related to target parameters such as biomass, leaf pigments, sugars, protein, and mineral contents. Due to their large effect on crop quality parameters, meteorological conditions and phenological stages were included as predictors in the models. We found that meteorological and phenological variables improved the accuracies and percentage of variance explained (R2) for most of the parameters evaluated. Based on R2 values, the best prediction models were obtained for biomass (0.71), sucrose (0.65), flavonoids (Flav) (0.56) and nitrogen (0.70) with normalized root mean squared errors of 0.196, 0.32, 0.087 and 0.08, respectively. These parameters were associated mainly with visible (VIS) (approx. 350–700 nm) and near infrared (NIR) (700–1250 nm) regions of the spectrum. Regarding mineral composition, the best prediction models were developed for P (0.51), B (0.50) and Zn (0.44), associated with the short-wave infra-red (SWIR) region (1250–2500 nm). The results of this study demonstrated the potential of hyperspectral techniques to be used as a base for performing initial evaluations in the field of quality traits in alfalfa crops. Hyperspectral imaging ; Canopy ; Spectrometer ; Biomass (ecology) ; Partial least squares regression ; Forage ; Phenology ; Environmental science ; Crop ; Near-infrared spectroscopy ; Remote sensing ; Mathematics ; Agronomy ; Botany ; Statistics ; Biology ; Optics ; Geography Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Galal Magdy | Amira A. Al-enna | Fathalla Belal | Ramadan A. El-Domany | Ahmed M. Abdel‐Megied
Source: BMC Chemistry (article, 2023-05-05)
Field: Chemistry — Analytical Chemistry

Analytical quality-by-design approach for development and validation of HPLC method for the simultaneous estimation of omarigliptin, metformin, and ezetimibe: application to human plasma and dosage forms A simple, selective, and sensitive RP-HPLC method was proposed for the simultaneous determination of two co-administered antidiabetic drugs (omarigliptin and metformin) with an anti-hyperlipidemic drug (ezetimibe) in a medicinally-recommended ratio of 2.5:50:1, respectively. The proposed procedure was optimized by adopting a quality-by-design approach. The influence of different factors on chromatographic responses was optimized by applying the two-level full factorial design (2<sup>5</sup>). The optimum chromatographic separation was achieved using Hypersil BDS C18 column at 45 °C, and the mobile phase pumped isocratically composed of methanol: potassium dihydrogen phosphate buffer (6.6 mM; pH 7, 67:33% v/v) at a flow rate of 0.814 mL/min using 235 nm as a detection wavelength. The developed method was capable of separating this novel mixture in less than 8 min. The calibration plots of omarigliptin, metformin, and ezetimibe showed acceptable linearity over the ranges of 0.2-2.0, 0.5-25.0, and 0.1-2.0 µg/mL with quantitation limits of 0.06, 0.50, and 0.06 µg/mL, respectively. The proposed method was successfully applied to determine the studied drugs in their commercial tablets with high % recoveries (96.8-102.92%) and low % RSD values (less than 2%). The applicability of the method was extended to the in-vitro assay of the drugs in spiked human plasma samples with high % recoveries (94.3-105.7%). The suggested method was validated in accordance with ICH guidelines. Ezetimibe ; Chromatography ; Chemistry ; Factorial experiment ; Quality by Design ; Calibration curve ; Phosphate buffered saline ; Detection limit ; Dosage form ; Metformin ; Chromatographic separation ; High-performance liquid chromatography ; Mathematics ; Statin ; Particle size ; Medicine Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Diabetes Treatment and Management
Authors: Monica T. Dayao | Alexandro E. Trevino | Honesty Kim | Matthew Ruffalo | H. Blaize D’Angio | Ryan Preska | Umamaheswar Duvvuri | Aaron T. Mayer | Ziv Bar‐Joseph
Source: Bioinformatics (article, 2023-06-01)
Field: Chemistry — Spectroscopy

Deriving spatial features from <i>in situ</i> proteomics imaging to enhance cancer survival analysis The code for this work can be found at gitlab.com/enable-medicine-public/spatsurv. Computer science ; Proteomics ; Spatial analysis ; Feature (linguistics) ; Spatial epidemiology ; Task (project management) ; Source code ; Machine learning ; Artificial intelligence ; Data mining ; Data science ; Biology ; Epidemiology ; Pathology ; Medicine ; Geography Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Bioinformatics and Genomic Networks
Authors: Maxime Ryckewaert | Daphné Heran | Jean-Philippe Trani | Silvia Mas-Garcia | Carole Feilhès | Fanny Prezman | Éric Serrano | Ryad Bendoula
Source: Scientific Data (article, 2023-10-26)
Field: Chemistry — Analytical Chemistry

Hyperspectral images of grapevine leaves including healthy leaves and leaves with biotic and abiotic symptoms A hyperspectral imaging database was collected on two hundred and five grape plant leaves. Leaves were measured with a hyperspectral camera in the visible/near infrared spectral range under controlled conditions. This dataset contains hyperspectral acquisition of grape leaves of seven different varieties. For each variety, acquisitions were performed on healthy leaves and leaves with foliar symptoms caused by different grapevine diseases showing clear symptoms of biotic or abiotic stress on other organs. For each leaf, chemical measurements such as chlorophyll and flavonol contents were also performed. Hyperspectral imaging ; Abiotic component ; Biotic stress ; Abiotic stress ; Botany ; Biology ; Vitis vinifera ; Horticulture ; Remote sensing ; Ecology ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement