jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lincoln Harris | William E. Fondrie | Sewoong Oh | William Stafford Noble
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-04-08)
Field: Chemistry — Spectroscopy

Evaluating proteomics imputation methods with improved criteria Abstract Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. This missingness hinders reproducibility, reduces statistical power, and makes it difficult to compare across samples or experiments. Although many methods exist for imputing missing values in proteomics data, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error, such as the mean-squared error between the imputed and the held-out observed values. Here we evaluate the performance of a set of commonly used imputation methods using three practical, “downstream-centric” criteria, which measure the ability of imputation methods to reconstruct differentially expressed peptides, identify new quantitative peptides, and improve peptide lower limit of quantification. Our evaluation spans several experiment types and acquisition strategies, including datadependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides, but that it can identify new quantitative peptides and improve peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that exisiting imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do. Imputation (statistics) ; Missing data ; Benchmarking ; Computer science ; Quantitative proteomics ; Data mining ; Proteomics ; Statistics ; Mathematics ; Machine learning ; Chemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Mieow Kee Chan | Kah Weng Lim | Alijah Mohd Aris | Yoke Ping Ho | Bee Chin Khor
Source: The Canadian Journal of Chemical Engineering (article, 2023-04-24)
Field: Chemistry — Organic Chemistry

Flow rate impact on the performance of immobilized <scp>nanoFeCu</scp> for sewage treatment and its reusability Abstract Ammonia pollution is a global issue, and it endangers human and aquatic life. The role of nanoFeCu in oxidizing ammonia has been identified, but its practicality and performance on the pilot scale are still unclear. This study was conducted to investigate immobilized nanoFeCu's performance in terms of ammonia, nitrate, nitrite, biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solid (TSS) by using sewage as feed. The effect of sewage flow rate was studied to determine the optimum operating condition of a pilot‐scale reactor. The reusability test was conducted to address sustainability concerns. The nanoFeCu was synthesized, immobilized in polymer clay, and placed into the reactor filled with sewage at a varied flow rate of from 210 to 1200 mL/min. Results showed that a higher sewage flow rate increased the ammonia removal rate within a shorter time but exhausted the immobilized nanoFeCu at a higher rate. Nitrate, nitrite, and TSS have a similar removal trend as ammonia. 800 mL/min was identified as the optimum flow rate with effluent ammonia concentration below 10 ppm in 9 h and maintained for 12 h. A reusability study showed that immobilized nanoFeCu could be reused for at least 10 successive cycles. The stability and performance of immobilized nanoFeCu suggested that it could be an alternative to treat sewage wastewater for real‐life applications. Effluent ; Sewage ; Wastewater ; Chemical oxygen demand ; Ammonia ; Nitrate ; Nitrite ; Sewage treatment ; Chemistry ; Biochemical oxygen demand ; Pulp and paper industry ; Environmental engineering ; Environmental science ; Organic chemistry ; Engineering Nanomaterials for catalytic reactions ; Environmental remediation with nanomaterials ; Adsorption and biosorption for pollutant removal
Authors: Maria Merin Antony | C. S. Suchand Sandeep | Hoong-Ta Lim | Murukeshan Vadakke Matham
Source: Journal of Biomedical Photonics & Engineering (article, 2023-07-27)
Field: Chemistry — Analytical Chemistry

High-Resolution Ultra-Spectral Imager for Advanced Imaging in Agriculture and Biomedical Applications Conventional imaging practices used for the inspection and monitoring of biological specimens employ RGB cameras with limited capabilities for early identification of diseases or abnormalities. In this work, we demonstrate and validate a quick, non-destructive, and precise inspection method utilizing an in-house developed push broom ultra-spectral imager. Precise image classification based on ultra-spectral signatures can provide fully automated machine vision capabilities, reducing inspection time, human errors, and man-hours. The proposed method has high spectral resolution (Δλ &lt; 1 nm) with 756 spectral bands, improved detection sensitivity, and high spatial resolution, which could potentially enable early-stage detection and accurate classification of abnormalities or diseases. Two potential applications of the developed system in agriculture and biomedical fields are demonstrated. Computer science ; Spectral imaging ; Artificial intelligence ; Computer vision ; Identification (biology) ; Image resolution ; Spectral resolution ; Precision agriculture ; RGB color model ; Sensitivity (control systems) ; Remote sensing ; Engineering ; Agriculture ; Physics ; Electronic engineering ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Enlai Gao | Hang Yang | Yongzhe Guo | Steven O. Nielsen | Ray H. Baughman
Source: Advanced Science (article, 2023-04-23)
Field: Materials Science — Materials Chemistry

The Stiffest and Strongest Predicted Material: C<sub>2</sub>N Atomic Chains Approach the Theoretical Limits Though linear atomic chains exhibit extreme properties, it is presently unclear how these properties can be maximized by the choice of elemental composition. Considering that boron, carbon, and nitrogen can form high modulus and high strength atomic chains, here an algorithm is developed to construct 143 possible atomic chains of these elements with 6 or fewer atoms in the primitive cell and explore their stabilities and mechanical properties by first-principles calculations. It is found that the gravimetric modulus (1032 GPa g<sup>-1</sup> cm<sup>3</sup> ) and strength (108 GPa g<sup>-1</sup> cm<sup>3</sup> ) of the C<sub>2</sub> N chain significantly exceed those of any known material, including the previously stiffest predicted material (C chain, 945 GPa g<sup>-1</sup> cm<sup>3</sup> ) and the previously strongest predicted material (BC chain, 92 GPa g<sup>-1</sup> cm<sup>3</sup> ), and also approach the theoretical limits of gravimetric modulus (1036 GPa g<sup>-1</sup> cm<sup>3</sup> ) and strength (130 GPa g<sup>-1</sup> cm<sup>3</sup> ). Mechanistic analyses demonstrate that the higher gravimetric modulus and strength of the C<sub>2</sub> N chain, compared with the C and BC chains, originate from its short, stiff chemical bonding and the abnormal decrease in bond length alternation under tension. The likely ease of fabrication and potential synthesis routes for C<sub>2</sub> N chains are discussed. Gravimetric analysis ; Modulus ; Materials science ; Chain (unit) ; Boron ; Bulk modulus ; Tension (geology) ; Young's modulus ; Chemistry ; Crystallography ; Composite material ; Thermodynamics ; Ultimate tensile strength ; Physics ; Organic chemistry Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Yanrong Chen | Yaozong Sun | Chongxue Bie | Xiaoli Wang | Xiaowei He | Xiaolei Song
Source: Quantitative Imaging in Medicine and Surgery (article, 2023-05-05)
Field: Materials Science — Materials Chemistry

Hierarchical K-means clustering method for accelerated Lorentzian estimation (KALE) in chemical exchange saturation transfer-magnetic resonance imaging quantification KALE quantification provides comparable or even superior contrast maps to traditional voxelwise fitting, with significantly reduced computation time. The 'smart' and hierarchical voxel-clustering and parameter updating process of KALE may facilitate more preclinical and clinical CEST applications. Saturation (graph theory) ; Cluster analysis ; Magnetic resonance imaging ; Estimation ; Nuclear magnetic resonance ; Computer science ; Biological system ; Physics ; Artificial intelligence ; Medicine ; Biology ; Mathematics ; Radiology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Muhammad Syahmi Hishamuddin | Shiou Yih Lee | Samsuddin Ahmad Syazwan | Shairul Izan Ramlee | Dhilia Udie Lamasudin | Rozi Mohamed
Source: 3 Biotech (article, 2023-02-07)
Field: Chemistry — Organic Chemistry

Highly divergent regions in the complete plastome sequences of Aquilaria are suitable for DNA barcoding applications including identifying species origin of agarwood products Agarwood ; DNA barcoding ; Biology ; Chloroplast DNA ; Threatened species ; Botany ; Evolutionary biology ; Ecology ; Phylogenetics Wood and Agarwood Research ; Identification and Quantification in Food ; Environmental DNA in Biodiversity Studies
Authors: Shreshtha Dhankar | Deepika Sharma | Hare Krishna Mohanta | Priya C. Sande
Source: Chemical Engineering & Technology (article, 2023-10-31)
Field: Chemistry — Analytical Chemistry

Machine Learning Applied to Predict Key Petroleum Crude Oil Constituents Abstract Sulfur compounds are the most important inorganic constituents of petroleum and require to be estimated beforehand because of their corrosive nature and other processing anomalies during crude oil processing. Paraffins, naphthene, and aromatics form the bulk of crude oil. Machine learning (ML) predictions of these constituents were made by training the ML model with a diverse industrial data set of 515 oils. The XGBoost model gave an excellent R 2 in the range 0.88–0.99 for the bulk compounds. R 2 for sulfur was in the modest range of 0.45–0.6, which improved significantly to 0.8 for additional inputs. ML applicability was thereby found to depend on the nature of the constituent. This work furthers ML‐based predictions, with the incentive of reducing expensive spectroscopic analytical methods. Crude oil ; Sulfur ; Petroleum ; Chemistry ; Petroleum engineering ; Range (aeronautics) ; Petroleum industry ; Biochemical engineering ; Pulp and paper industry ; Process engineering ; Chromatography ; Organic chemistry ; Environmental science ; Materials science ; Engineering ; Environmental engineering Spectroscopy and Chemometric Analyses ; Petroleum Processing and Analysis ; Computational Drug Discovery Methods
Authors: German Samolyuk | Eva Zarkadoula | C. Lau | Atul Kumar | J. Rapp | Markus Eisenbach | Yuri Osetskiy
Source: Materialia (article, 2023-09-04)
Field: Materials Science — Materials Chemistry

Crystallographic and temperature effects in low-energy collisions for plasma–material interactions Materials science ; Tungsten ; Plasma ; Deuterium ; Ion ; Penetration depth ; Penetration (warfare) ; Atomic physics ; Nuclear physics ; Physics ; Optics Fusion materials and technologies ; Nuclear Materials and Properties ; Ion-surface interactions and analysis
Authors: Isao Nakatsugawa | Koji Yamada | Tomoko Yamashita | Takashi Nishikawa | Yasuhito Taniguchi | Yasumasa Chino
Source: Journal of The Electrochemical Society (article, 2023-09-01)
Field: Materials Science — Biomaterials

Effect of Al and Ca Elements on the Atmospheric Corrosion of Field-Exposed Mg–Al–Zn–Ca–Mn Magnesium Alloys Three different extruded Mg–Al–Zn–Ca–Mn magnesium alloys with varying alloying elements and a diecast AZ91D alloy were exposed to two coastal environments for five years. Under unsheltered exposure, Mg–Al type layered double hydroxide was primarily formed, and the mass loss corrosion rate was decreased with increasing Al content in the alloys. Under sheltered exposure, magnesium hydroxy carbonate was the main corrosion product. Additionally, Mg alloys with high Al content tended to exhibit localized corrosion. Furthermore, the negative impact of Ca addition on atmospheric corrosion occurred when the alloys were exposed under sheltered conditions. Corrosion ; Magnesium ; Metallurgy ; Materials science ; Hydroxide ; Alloy ; Magnesium alloy ; Inorganic chemistry ; Chemistry Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Aluminum Alloys Composites Properties
Authors: Paul Seibert | Alexander Raßloff | Karl A. Kalina | Ali Reza Safi | Paul Reck | Daniel Peterseim | Benjamin Klusemann | Markus Kästner
Source: PAMM (article, 2023-09-15)
Field: Materials Science — Materials Chemistry

On the relevance of descriptor fidelity in microstructure reconstruction Abstract A common strategy for reducing the computational effort of descriptor‐based microstructure reconstruction in the Yeong–Torquato algorithm lies in restricting the choice of descriptors to an efficiently computable subset. As an alternative, the number of iterations can be reduced by gradient‐based optimization as in differentiable microstructure characterization and reconstruction (DMCR). This allows for, but does not require, the use of a set of informative, high‐dimensional and computationally expensive descriptors that would be unfeasible for a high number of iterations. For this reason, the present work investigates the role of descriptor fidelity on microstructure reconstruction results. More precisely, spatial two‐ and three‐point correlations as well as the lineal path function are computed on 2D planes as well as on 1D lines. These descriptors are used for reconstruction with the Yeong–Torquato and DMCR algorithm and the results are compared throughout various microstructures, respectively. Differentiable function ; Relevance (law) ; Set (abstract data type) ; Fidelity ; Algorithm ; Function (biology) ; Path (computing) ; Mathematics ; Point (geometry) ; Computer science ; Mathematical optimization ; Artificial intelligence ; Geometry ; Mathematical analysis Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques
Authors: Erika Nagano | K Odake | Shuichi Shimma
Source: Mass Spectrometry (article, 2023-07-19)
Field: Chemistry — Spectroscopy

An Alternative Method for Quantitative Mass Spectrometry Imaging (&lt;i&gt;q&lt;/i&gt;-MSI) of Dopamine Utilizing Fragments of Animal Tissue Mass spectrometry imaging (MSI) is a well-known method for the ionization of molecules on tissue sections and the visualization of their localization. Recently, different sample preparation methods and new instruments have been used for MSI, and different molecules are becoming visible. On the other hand, although several quantification methods (<i>q</i>-MSI) have been proposed, there is still room for the development of a simplified procedure. Here, we have attempted to develop a reproducible and reliable quantification method using a calibration curve prepared from tissue debris of a frozen section of a sample when we trim the frozen blocks. We discuss the reproducibility of this method across different sample lots and the effect of the biological matrix (ion suppression) on our results. The quantitative performance was evaluated in terms of accuracy and relative standard deviation, and the reliability of the quantitative values obtained by matrix-assisted laser desorption/ionization-MSI was further evaluated by enzyme-linked immunosorbent assay (ELISA). Our <i>q</i>-MSI method for the quantification of dopamine in mouse brain tissue was found to be highly linear, accurate, and precise. The quantitative values obtained by MSI were found to be highly comparable (>85% similarity) to the results obtained by ELISA from the same tissue extracts. Mass spectrometry imaging ; Mass spectrometry ; Reproducibility ; Sample (material) ; Matrix (chemical analysis) ; Sample preparation ; MALDI imaging ; Quantitative analysis (chemistry) ; Chemistry ; Chromatography ; Analytical Chemistry (journal) ; Matrix-assisted laser desorption/ionization ; Desorption Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Advanced Proteomics Techniques and Applications
Authors: Yu Ishibashi | Ryota Haraguchi | Shigehisa Aoki | Yushi Oishi | Takayuki Narita
Source: Gels (article, 2023-06-02)
Field: Materials Science — Biomaterials

Effect of UV Irradiation of Pre-Gel Solutions on the Formation of Collagen Gel Tubes Hollow collagen gels are promising materials for drug/cell delivery systems to promote tissue regeneration because they may be able to function as carriers for these types of loads. Controlling the cavity size and swelling suppression is essential to expand the applications and improve the usability of such gel-like systems. We investigated the effects of UV-treated collagen solutions as a pre-gel aqueous mixture on the formation and properties of the hollow collagen gels in terms of their preparation range limits, morphology, and swelling ratio. The UV treatment thickened the pre-gel solutions, which allowed hollowing at lower collagen concentrations. This treatment also prevents the over-swelling of the hollow collagen rods in PBS buffer solutions. The UV-treated collagen solutions provided a large lumen space in the prepared collagen hollow fiber rods with a limited swelling ratio, allowing vascular endothelial cells and ectodermal cells to be cultured separately in the outer and inner lumen. Swelling ; Rod ; Biophysics ; Irradiation ; Materials science ; Aqueous solution ; Drug delivery ; Self-healing hydrogels ; Morphology (biology) ; Buffer (optical fiber) ; Type I collagen ; Chemical engineering ; Chemistry ; Biomedical engineering ; Polymer chemistry ; Nanotechnology ; Composite material ; Organic chemistry Collagen: Extraction and Characterization ; 3D Printing in Biomedical Research ; Bone Tissue Engineering Materials
Authors: Amornrit Puttipipatkajorn | Anupun Terdwongworakul | Amorndej Puttipipatkajorn | Supachai Kulmutiwat | Peerapong Sangwanangkul | Thana Cheepsomsong
Source: IEEE Access (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Indirect Prediction of Dry Matter in Durian Pulp With Combined Features Using Miniature NIR Spectrophotometer Immature durian poses a crucial problem for durian export. A miniature near-infrared spectrometer was used to develop a prediction model based on fruit stem and rind spectra. A sample of 120 durian fruits with variation in maturity was used to calibrate the model using different machine-learning algorithms, consisting of partial least squares regression (PLSR), least square support vector machine (LS-SVM), and artificial neural network (ANN) approaches. Initially, the rind model provided better predictive performance than the stem model for all algorithms. However, combining either the stem or the rind spectra (or even the fruit density) as the independent variables, the built models produced better results in prediction than from using either the stem or rind spectra alone. Among the models investigated, the combined spectra model of the stem and rind without spike spectra and using the ANN algorithm produced the best prediction with a correlation coefficient of 0.848, a root mean square error of prediction of 2.215%, and a residual predictive deviation of 1.833. Finally, the results showed that the proposed model performed sufficiently well in predicting the dry matter content of durian fruit using the miniature near-infrared spectrometer, even though it underperformed the model derived from the durian pulp. Dry matter ; Pulp (tooth) ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Industrial Vision Systems and Defect Detection
Authors: Haowen Zhang | Qinghai He | Chongshan Yang | Min Lu | Zhongyuan Liu | Xiaojia Zhang | Xiaoli Li | Chunwang Dong
Source: Sensors (article, 2023-12-07)
Field: Chemistry — Analytical Chemistry

Research on the Detection Method of Organic Matter in Tea Garden Soil Based on Image Information and Hyperspectral Data Fusion Soil organic matter is an important component that reflects soil fertility and promotes plant growth. The soil of typical Chinese tea plantations was used as the research object in this work, and by combining soil hyperspectral data and image texture characteristics, a quantitative prediction model of soil organic matter based on machine vision and hyperspectral imaging technology was built. Three methods, standard normalized variate (SNV), multisource scattering correction (MSC), and smoothing, were first used to preprocess the spectra. After that, random frog (RF), variable combination population analysis (VCPA), and variable combination population analysis and iterative retained information variable (VCPA-IRIV) algorithms were used to extract the characteristic bands. Finally, the quantitative prediction model of nonlinear support vector regression (SVR) and linear partial least squares regression (PLSR) for soil organic matter was established by combining nine color features and five texture features of hyperspectral images. The outcomes demonstrate that, in comparison to single spectral data, fusion data may greatly increase the performance of the prediction model, with MSC + VCPA-IRIV + SVR (R<sup>2</sup>C = 0.995, R<sup>2</sup>P = 0.986, RPD = 8.155) being the optimal approach combination. This work offers excellent justification for more investigation into nondestructive methods for determining the amount of organic matter in soil. Hyperspectral imaging ; Smoothing ; Partial least squares regression ; Soil texture ; Soil organic matter ; Organic matter ; Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Population ; Mathematics ; Random forest ; Computer science ; Soil science ; Statistics ; Environmental science ; Soil water ; Ecology ; Biology Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Metabolomics and Mass Spectrometry Studies