jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nienke Köllmann | Floor K.G. Schreuders | Puneet Mishra | Lu Zhang | Atze Jan van der Goot
Source: Journal of Food Composition and Analysis (article, 2023-05-24)
Field: Chemistry — Analytical Chemistry

Near-infrared spectroscopy-based quantification of sunflower oil and pea protein isolate in dense mixtures for novel plant-based products Techniques to quantify oil and protein in plant-based products are laborious and environmentally harmful. This study explores the potential of near-infrared (NIR) spectroscopy as an alternative method for rapid and non-destructive quantification of oil and protein in mixtures with known content of sunflower oil and pea protein isolate (PPI). Accurate calibrations with partial least square regression (PLSR) were possible for sunflower oil (Root Mean Standard Error of the Test (RMSET) set, 0.33%; coefficient of determination of the test set (R2t), 0.99) and PPI content (RMSET, 1.24%; R2t, 0.99). Prediction of an extrapolated validation set was less accurate for sunflower oil (Root Mean Standard Error of the Validation (RMSEV) set, 2.65%; coefficient of determination of the validation set (R2v), 0.70), but was successful for PPI content (RMSEV, 2.43%; R2v, 0.93). Wavelength range reduction for CH/NH bonds improved the predictions of the PLSR models for oil but not for PPI. Wavelength selection using the Covariance Selection (CovSel) algorithm reduced the number of wavelengths from 2151 to 3 without loss of prediction accuracy within the calibration range. Wavelength range reduction gave the best model for oil; wavelength selection was best for PPI. NIR spectroscopy is a promising tool to determine oil and protein in plant-based foods rapidly and with sufficient accuracy. Sunflower oil ; Near-infrared spectroscopy ; Sunflower ; Calibration ; Partial least squares regression ; Correlation coefficient ; Mean squared error ; Test set ; Coefficient of determination ; Spectroscopy ; Biological system ; Chemistry ; Mathematics ; Analytical Chemistry (journal) ; Chromatography ; Statistics ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Hala Salim Sonbol
Source: Journal of Medical Sciences and Interdisciplinary Research (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Nutritional Proteomics: A Pathway to Understanding and Optimizing Human Health Proteomics is a branch of science that focuses on the study of proteins—how they are structured, function, and interact in living organisms. This review article explores the various aspects of proteomics and its broad applications in various research areas. It emphasizes the pivotal role the proteome plays in an organism, with changes influenced by its physiological state and environmental factors. Nutritional proteomics, or neuroproteomics, applies proteomic techniques to study how proteins int Proteomics ; Human health ; Proteome ; Human proteome project ; MEDLINE Advanced Proteomics Techniques and Applications ; Nutrition, Genetics, and Disease ; Metabolomics and Mass Spectrometry Studies
Authors: Qiong Cao | Chunjiang Zhao | Bingnan Bai | Jie Cai | Longyue Chen | Fan Wang | Bo Xu | Dandan Duan | Ping Jiang | Xiangyu Meng | Guijun Yang
Source: Frontiers in Plant Science (article, 2023-08-29)
Field: Chemistry — Analytical Chemistry

Oolong tea cultivars categorization and germination period classification based on multispectral information Recognizing and identifying tea plant (<i>Camellia sinensis</i>) cultivar plays a significant role in tea planting and germplasm resource management, particularly for oolong tea. There is a wide range of high-quality oolong tea with diverse varieties of tea plants that are suitable for oolong tea production. The conventional method for identifying and confirming tea cultivars involves visual assessment. Machine learning and computer vision-based automatic classification methods offer efficient and non-invasive alternatives for rapid categorization. Despite advancements in technology, the identification and classification of tea cultivars still pose a complex challenge. This paper utilized machine learning approaches for classifying 18 oolong tea cultivars based on 27 multispectral characteristics. Then the SVM classification model was executed using three optimization algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimizer (GWO). The results revealed that the SVM model optimized by GWO achieved the best performance, with an average discrimination rate of 99.91%, 93.30% and 92.63% for the training set, test set and validation set, respectively. In addition, based on the multispectral information (h, s, r, b, L, Asm, Var, Hom, Dis, σ, S, G, RVI, DVI, VOG), the germination period of oolong tea cultivars can be completely evaluated by Fisher discriminant analysis. The study indicated that the practical protection of tea plants through automated and precise classification of oolong tea cultivars and germination periods is feasible by utilizing multispectral imaging system. Camellia sinensis ; Cultivar ; Multispectral image ; Artificial intelligence ; Germplasm ; Machine learning ; Mathematics ; Pattern recognition (psychology) ; Computer science ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote Sensing and Land Use
Authors: David Heinrich Müller | Carsten Flake | Thorsten Brands | Hans‐Jürgen Koß
Source: Biotechnology and Bioengineering (article, 2023-05-11)
Field: Chemistry — Analytical Chemistry

Bioprocess in‐line monitoring using Raman spectroscopy and Indirect Hard Modeling (IHM): A simple calibration yields a robust model To increase the process productivity and product quality of bioprocesses, the in-line monitoring of critical process parameters is highly important. For monitoring substrate, metabolite, and product concentrations, Raman spectroscopy is a commonly used Process Analytical Technology (PAT) tool that can be applied in-situ and non-invasively. However, evaluating bioprocess Raman spectra with a robust state-of-the-art statistical model requires effortful model calibration. In the present study, we in-line monitored a glucose to ethanol fermentation by Saccharomyces cerevisiae (S. cerevisiae) using Raman spectroscopy in combination with the physics-based Indirect Hard Modeling (IHM) and showed successfully that IHM is an alternative to statistical models with significantly lower calibration effort. The IHM prediction model was developed and calibrated with only 16 Raman spectra in total, which did not include any process spectra. Nevertheless, IHM's root mean square errors of prediction (RMSEPs) for glucose (3.68 g/L) and ethanol (1.69 g/L) were comparable to the prediction quality of similar studies that used statistical models calibrated with several calibration batches. Despite our simple calibration, we succeeded in developing a robust model for evaluating bioprocess Raman spectra. Bioprocess ; Calibration ; Raman spectroscopy ; Process analytical technology ; Biological system ; Process (computing) ; Chemometrics ; Process engineering ; Biochemical engineering ; Chemistry ; Analytical Chemistry (journal) ; Computer science ; Chromatography ; Mathematics ; Engineering ; Statistics ; Physics ; Optics ; Biology Spectroscopy and Chemometric Analyses ; Viral Infectious Diseases and Gene Expression in Insects ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: William S. Phipps | Mark Kilgore | Jacob J. Kennedy | Jeffrey R. Whiteaker | Andrew N. Hoofnagle | Amanda G. Paulovich
Source: Molecular & Cellular Proteomics (article, 2023-09-19)
Field: Chemistry — Spectroscopy

Clinical Proteomics for Solid Organ Tissues The evaluation of biopsied solid organ tissue has long relied on visual examination using a microscope. Immunohistochemistry is critical in this process, labeling and detecting cell lineage markers and therapeutic targets. However, while the practice of immunohistochemistry has reshaped diagnostic pathology and facilitated improvements in cancer treatment, it has also been subject to pervasive challenges with respect to standardization and reproducibility. Efforts are ongoing to improve immunohistochemistry, but for some applications, the benefit of such initiatives could be impeded by its reliance on monospecific antibody-protein reagents and limited multiplexing capacity. This perspective surveys the relevant challenges facing traditional immunohistochemistry and describes how mass spectrometry, particularly liquid chromatography-tandem mass spectrometry, could help alleviate problems. In particular, targeted mass spectrometry assays could facilitate measurements of individual proteins or analyte panels, using internal standards for more robust quantification and improved interlaboratory reproducibility. Meanwhile, untargeted mass spectrometry, showcased to date clinically in the form of amyloid typing, is inherently multiplexed, facilitating the detection and crude quantification of 100s to 1000s of proteins in a single analysis. Further, data-independent acquisition has yet to be applied in clinical practice, but offers particular strengths that could appeal to clinical users. Finally, we discuss the guidance that is needed to facilitate broader utilization in clinical environments and achieve standardization. Standardization ; Computer science ; Immunohistochemistry ; Computational biology ; Mass spectrometry ; Analyte ; Mass spectrometry imaging ; Tandem mass spectrometry ; Proteomics ; Medicine ; Pathology ; Biology ; Chemistry ; Chromatography ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Johanna Thiery | Matthias Fahrner
Source: PROTEOMICS (article, 2023-12-24)
Field: Chemistry — Spectroscopy

Integration of proteomics in the molecular tumor board Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration. Proteomics ; Computational biology ; Precision oncology ; Molecular diagnostics ; Personalized medicine ; Genomics ; Molecular pathology ; Omics ; Biology ; Bioinformatics ; Cancer ; Gene ; Genome ; Genetics Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; Cancer Genomics and Diagnostics
Authors: AbdulAziz Ascandari | Suleiman Aminu | Nour El Houda Safdi | Achraf El Allali | Rachid Daoud
Source: Frontiers in Microbiology (article, 2023-07-05)
Field: Chemistry — Spectroscopy

A bibliometric analysis of the global impact of metaproteomics research The scientific outputs of metaproteomics are rapidly evolving with developed countries leading the way. Although Africa showed prospects for future progress, this could only be accelerated by providing funding, increased collaborations, and mentorship programs. Metaproteomics ; Scopus ; Bibliometrics ; Web of science ; Citation ; Library science ; Scientometrics ; Political science ; Data science ; Biology ; Computer science ; MEDLINE ; Metagenomics Advanced Proteomics Techniques and Applications ; Genetics, Bioinformatics, and Biomedical Research ; Scientific Computing and Data Management
Authors: Sushmita Basu | Bi Zhao | Bálint Biró | Eshel Faraggi | Jörg Gsponer | Gang Hu | Andrzej Kloczkowski | Nawar Malhis | Milot Mirdita | Johannes Söding | Martin Steinegger | Duolin Wang | Kui Wang | Dong Xu | Jian Zhang | Lukasz Kurgan
Source: Nucleic Acids Research (article, 2023-11-02)
Field: Chemistry — Spectroscopy

DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options The DescribePROT database of amino acid-level descriptors of protein structures and functions was substantially expanded since its release in 2020. This expansion includes substantial increase in the size, scope, and quality of the underlying data, the addition of experimental structural information, the inclusion of new data download options, and an upgraded graphical interface. DescribePROT currently covers 19 structural and functional descriptors for proteins in 273 reference proteomes generated by 11 accurate and complementary predictive tools. Users can search our resource in multiple ways, interact with the data using the graphical interface, and download data at various scales including individual proteins, entire proteomes, and whole database. The annotations in DescribePROT are useful for a broad spectrum of studies that include investigations of protein structure and function, development and validation of predictive tools, and to support efforts in understanding molecular underpinnings of diseases and development of therapeutics. DescribePROT can be freely accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/. Proteome ; Download ; Interface (matter) ; Graphical user interface ; Biology ; Function (biology) ; Scope (computer science) ; Computer science ; Database ; Computational biology ; Bioinformatics ; World Wide Web ; Operating system Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics
Authors: Issam Barra | Tarik El Moatassem | Fassil Kebede
Source: Sensors (article, 2023-11-14)
Field: Chemistry — Analytical Chemistry

Soil Particle Size Thresholds in Soil Spectroscopy and Its Effect on the Multivariate Models for the Analysis of Soil Properties This study focused on one of the few but critical sample preparations required in soil spectroscopy (i.e., grinding), as well as the effect of soil particle size on the FTIR spectral database and the partial least squares regression models for the prediction of eight soil properties (viz., TC, TN, OC, sand, silt, clay, Olsen P, and CEC). Fifty soil samples from three Moroccan region were used. The soil samples underwent three preparations (drying, grinding, sieving) to obtain, at the end of the sample preparation step, three ranges of particle size, samples with sizes < 500 µm, samples with sizes < 250 µm, and a third range with particles < 125 µm. The multivariate models (PLSR) were set up based on the FTIR spectra recorded on the different obtained samples. The correlation coefficient (R<sup>2</sup>) and the root mean squared error of cross validation (RMSECV) were chosen as figures of merit to assess the quality of the prediction models. The results showed a general trend in improving the R2 as the finer particles were used (from <500 µm to 125 µm), which was clearly observed for TC, TN, P<sub>2</sub>O<sub>5</sub>, and CEC, whereas the cross-validation errors (RMSECV) showed an opposite trend. This confirmed that fine soil grinding improved the accuracy of predictive models for soil properties diagnosis in soil spectroscopy. Multivariate statistics ; Silt ; Partial least squares regression ; Soil test ; Particle size ; Soil science ; Grinding ; Linear regression ; Analytical Chemistry (journal) ; Mathematics ; Soil water ; Mineralogy ; Chemistry ; Environmental science ; Materials science ; Statistics ; Environmental chemistry ; Geology ; Metallurgy Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Mineral Processing and Grinding
Authors: Sifan Ding | Xiaobin Chen | Yong Xu | Wenhui Duan
Source: npj Computational Materials (article, 2023-10-14)
Field: Materials Science — Materials Chemistry

The best thermoelectrics revisited in the quantum limit Abstract The classical problem of best thermoelectrics, which was believed originally solved by Mahan and Sofo [Proc. Natl. Acad. Sci. USA 93, 7436 (1996)], is revisited and discussed in the quantum limit. We express the thermoelectric figure of merit ( z T ) as a functional of electronic transmission probability $${{{\mathcal{T}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> by the Landauer–Büttiker formalism, which is able to deal with thermoelectric transport ranging from ballistic to diffusive regimes. We also propose to apply the calculus of variations to search for the optimal $${{{\mathcal{T}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> giving the maximal z T . Our study reveals that the optimal transmission probability $${{{\mathcal{T}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> is a boxcar function instead of a delta function proposed by Mahan and Sofo, leading to z T exceeding the well-known Mahan–Sofo limit. Furthermore, we suggest realizing the optimal $${{{\mathcal{T}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> in topological material systems. Our work defines the theoretical upper limit for quantum thermoelectrics, which is of fundamental significance to the future development of thermoelectrics. Limit (mathematics) ; Algorithm ; Thermoelectric materials ; Function (biology) ; Figure of merit ; Physics ; Computer science ; Mathematics ; Quantum mechanics ; Thermoelectric effect ; Mathematical analysis Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Advanced Thermodynamics and Statistical Mechanics
Authors: Ashley Phetsanthad | Austin V. Carr | Lauren Fields | Lingjun Li
Source: Journal of Proteome Research (article, 2023-03-15)
Field: Chemistry — Spectroscopy

Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520. Computer science ; Computational biology ; Identification (biology) ; Workflow ; Instrumentation (computer programming) ; Biology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Identification and Quantification in Food
Authors: Łukasz Szyrwiel | Christoph Gille | Michael Mülleder | Vadim Demichev | Markus Ralser
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-02-19)
Field: Chemistry — Spectroscopy

Speedy-PASEF: Analytical flow rate chromatography and trapped ion mobility for deep high-throughput proteomics Abstract Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs and facilitate new approaches in systems biology and biomedical research. Here we propose Speedy-PASEF, a combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition and data analysis with the DIA-NN software suite, for conducting fast, high-quality proteomic experiments that require only moderate sample amounts. For instance, using a 500-μl/min flow rate and a 3-minute chromatographic gradient, Speedy-PASEF quantified 5,211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used Speedy-PASEF to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-minute chromatographic gradient and alternating column regeneration on a dual pump system, for processing 398 samples per day. Speedy-PASEF delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates. Speedy-PASEF thus facilitates acquisition of high-quality proteomes in large numbers. Proteome ; Chromatography ; Volumetric flow rate ; Throughput ; Proteomics ; Biomarker discovery ; Chemistry ; Computer science ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Protein purification and stability