jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Diana M. Rojas‐González | Aaron Babendreyer | Andreas Ludwig | Petra Mela
Source: Scientific Reports (article, 2023-09-01)
Field: Materials Science — Biomaterials

Analysis of flow-induced transcriptional response and cell alignment of different sources of endothelial cells used in vascular tissue engineering Endothelialization of tissue-engineered vascular grafts has proven crucial for implant functionality and thus clinical outcome, however, the choice of endothelial cells (ECs) is often driven by availability rather than by the type of vessel to be replaced. In this work we studied the response to flow of different human ECs with the aim of examining whether their response in vitro is dictated by their original in vivo conditions. Arterial, venous, and microvascular ECs were cultured under shear stress (SS) of 0, 0.3, 3, 1, 10, and 30 dyne/cm<sup>2</sup> for 24 h. Regulation of flow-induced marker KLF2 was similar across the different ECs. Upregulation of anti-thrombotic markers, TM and TPA, was mainly seen at higher SS. Cell elongation and alignment was observed for the different ECs at 10 and 30 dyne/cm<sup>2</sup> while at lower SS cells maintained a random orientation. Downregulation of pro-inflammatory factors SELE, IL8, and VCAM1 and up-regulation of anti-oxidant markers NQO1 and HO1 was present even at SS for which cell alignment was not observed. Our results evidenced similarities in the response to flow among the different ECs, suggesting that the maintenance of the resting state in vitro is not dictated by the SS typical of the tissue of origin and that absence of flow-induced cell orientation does not necessarily correlate with a pro-inflammatory state of the ECs. These results support the use of ECs from easily accessible sources for in vitro vascular tissue engineering independently from the target vessel. Downregulation and upregulation ; In vivo ; In vitro ; Endothelial stem cell ; Cell biology ; Flow cytometry ; Tissue engineering ; KLF2 ; Cell ; Endothelium ; Cell type ; Vascular tissue ; Chemistry ; Biology ; Immunology ; Biomedical engineering ; Medicine ; Endocrinology ; Biochemistry ; Biotechnology Electrospun Nanofibers in Biomedical Applications ; Angiogenesis and VEGF in Cancer ; Coronary Interventions and Diagnostics
Authors: Leah Munyendo | Majharulislam Babor | Yanyan Zhang | Bernd Hitzmann
Source: Journal of Food Measurement & Characterization (article, 2023-10-12)
Field: Chemistry — Analytical Chemistry

Chemometrics using near-infrared spectra for the quantification of robusta coffee and chicory added as adulterants in roasted arabica coffee Abstract Roasted ground coffees are targets of concern regarding intentional adulteration with cheaper foreign materials because, in this form, it may be difficult to detect due to the small particle size and the dark color. Therefore, a significant interest is developing fast, sensitive, and accurate methodologies to quantify adulterants in roasted coffees. This study investigated the potential of using near-infrared spectroscopy (NIR) to quantity robusta coffee and chicory in roasted arabica coffee. The adulterated arabica coffee samples were composed of robusta coffee or chicory ranging from 2.5 to 30% in increments of 2.5%. Four regression approaches were applied: gradient boosting regression (GBR), multiple linear regression (MLR), k-nearest neighbor regression (KNNR), and partial least squares regression (PLSR). The first three regression models were performed on the features extracted from linear discriminant analysis (LDA) or principal component analysis (PCA). Additionally, two classification methods were applied (LDA and KNN). The regression models derived based on LDA-extracted features presented better performances than PCA ones. The best regression models for the quantification of robusta coffee were GBR (pRMSEP of 13.70% and R 2 of 0.839) derived based on PCA-extracted features and MLR (pRMSEP of 1.11% and R 2 of 0.998) derived based on LDA-extracted features. For the chicory quantification, the same models derived under the same settings as mentioned above also presented the best performances (GBR, pRMSEP = 9.37%, R 2 = 0.924; MLR, pRMSEP = 1.54%, R 2 = 0.997). The PLSR prediction errors for the quantification of arabica coffee and chicory were 9.90% and 8.09%, respectively. For the classification methods, the LDA model performed well compared to KNN. Generally, some models proved to be effective in quantifying robusta and chicory in roasted arabica coffee. The results of this study indicate that NIR spectroscopy could be a promising method in the coffee industry and other legal sectors for routine applications involving quality control of coffee. Linear discriminant analysis ; Partial least squares regression ; Principal component analysis ; Mathematics ; Chemometrics ; Linear regression ; Regression analysis ; Coffea arabica ; Principal component regression ; Arabica coffee ; Statistics ; Food science ; Pattern recognition (psychology) ; Chemistry ; Artificial intelligence ; Chromatography ; Botany ; Horticulture ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Coffee research and impacts
Authors: Yulia Zakharova | Artyom Marchenkov | Darya P. Petrova | Yurij Bukin | Alexey A. Morozov | Yekaterina D. Bedoshvili | Yulia А. Podunay | О. И. Давидович | Н. А. Давидович | Alexander A. Bondar | Maria Kahlert | Yelena V. Likhoshway
Source: Diversity (article, 2023-02-13)
Field: Materials Science — Biomaterials

Delimitation of Some Taxa of Ulnaria and Fragilaria (Bacillariophyceae) Based on Genetic, Morphological Data and Mating Compatibility Fragilaria and Ulnaria are two closely related diatom genera for which the delimitation and circumscription of several species is unclear. We studied strains isolated from Lake Baikal and compared them with the species from freshwater reservoirs in Europe and Asia using phylogenetic and species delimitation methods, microscopy and interclonal crossing experiments. The results of the phylogenetic analyses of the fragments of rbcL and 18S rRNA genes revealed that baikalian F. radians clade was independent from the representatives of the genus from other localities. Among Ulnaria we found the following 18S rRNA phylogenetic tree groups at species level: U. acus, U. ulna and U. danica. Genetic distance between genera varied between 3.9–10.2% substitutions in rbcL gene and 3.2–11.5% in 18S rRNA. The boundary between intraspecies and interspecies polymorphism for studied species of Ulnaria and Fragilaria in these marker genes was around 0.8% substitutions. Morphometric characters of individual strains showed their variability and division into F. radians, U. acus and U. ulna together with U. danica. Strains of U. acus and U. danica from different localities of Europe and Asia were sexually compatible inside the species. Sexual reproduction has never been observed in monoclonal cultures, either between this species or with strains of the Fragilaria. Biology ; Phylogenetic tree ; Fragilaria ; Zoology ; Taxon ; 18S ribosomal RNA ; Evolutionary biology ; Clade ; Botany ; Ecology ; Genetics ; Gene Diatoms and Algae Research ; Microbial Community Ecology and Physiology ; Analytical chemistry methods development
Authors: Klára Brožová | Brigitte Hantusch | Lukas Kenner | Klaus Kratochwill
Source: Proteomes (review, 2023-05-03)
Field: Chemistry — Spectroscopy

Spatial Proteomics for the Molecular Characterization of Breast Cancer Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease's heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to identify early diagnostic biomarkers and therapeutic targets, and to understand protein expression levels and modifications. The subcellular localization of proteins is a key factor in their physiological function, making the study of subcellular localization a major challenge in cell biology. Achieving high resolution at the cellular and subcellular level is essential for obtaining an accurate spatial distribution of proteins, which in turn can enable the application of proteomics in clinical research. In this review, we present a comparison of current methods of spatial proteomics in BC, including untargeted and targeted strategies. Untargeted strategies enable the detection and analysis of proteins and peptides without a predetermined molecular focus, whereas targeted strategies allow the investigation of a predefined set of proteins or peptides of interest, overcoming the limitations associated with the stochastic nature of untargeted proteomics. By directly comparing these methods, we aim to provide insights into their strengths and limitations and their potential applications in BC research. Proteomics ; Computational biology ; Leverage (statistics) ; Biology ; Quantitative proteomics ; Breast cancer ; Subcellular localization ; Population ; Bioinformatics ; Computer science ; Cancer ; Medicine ; Cell biology ; Genetics ; Artificial intelligence Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Glycosylation and Glycoproteins Research
Authors: Lilian R. Heil | Philip M. Remes | Jesse D. Canterbury | Ping Yip | William D. Barshop | Christine C. Wu | Michael J. MacCoss
Source: Analytical Chemistry (article, 2023-08-01)
Field: Chemistry — Spectroscopy

Dynamic Data-Independent Acquisition Mass Spectrometry with Real-Time Retrospective Alignment Data-independent acquisition (DIA) mass spectrometry has grown in popularity in recent years, because of the reproducibility and quantitative rigor of a systematic tandem mass spectrometry (MS/MS) sampling method. However, traditional DIA methods may spend valuable instrument time acquiring MS/MS spectra with no usable information in them, affecting sensitivity and quantitative performance. We developed a DIA strategy that dynamically adjusts the MS/MS windows during the chromatographic separation. The method focuses MS/MS acquisition on the most relevant mass range at each point in time─increasing the quantitative sensitivity by increasing the time spent on each DIA window. We demonstrate an improved lower limit of quantification, on average, without sacrificing the number of peptides detected. Chemistry ; Mass spectrometry ; Tandem mass spectrometry ; Reproducibility ; Chromatography ; Analytical Chemistry (journal) ; Data acquisition ; Computer science Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Xiaoyan Liu | Mingxin Zhang | Chen Shao | Haidan Sun | Binbin Zhang | Zhengguang Guo | Jiameng Sun | Qi Feng | Yushi Zhang | Haitao Niu | Wei Sun
Source: Molecular & Cellular Proteomics (article, 2023-06-20)
Field: Chemistry — Spectroscopy

Blood- and Urine-Based Liquid Biopsy for Early-Stage Cancer Investigation: Taken Clear Renal Cell Carcinoma as a Model Liquid biopsy is a noninvasive technique that can provide valuable information for disease characterization by using biofluids as a source of biomarkers. Proteins found in biofluids can offer a wealth of information for understanding pathological processes. In this study, we used early-stage clear cell renal cell carcinoma (ccRCC) as a model to explore the proteomic relationships among tissue, plasma, and urine. We analyzed samples of tumor tissue, plasma, and urine from a cohort of 27 ccRCC patients with T1-2 stage and 27 matched healthy controls, using liquid chromatography-mass spectrometry (LC-MS) for proteomic analysis. We integrated the differential proteins found in the three types of samples to explore ccRCC-associated molecular changes. Our results showed that both plasma and urine proteomes could reflect functional changes in tumor tissue. In plasma, cytoskeletal proteins and metabolic enzymes were differentially expressed, while in urine, adhesion molecules and defense proteins showed differential levels. The differential proteins found in plasma and urine both reflect the binding and catalytic activity of tumor tissue. Additionally, proteins only changed in biofluids could reflect body immune response changes, with plasma proteins involved in actin cytoskeleton and oxidative stress, and urine proteins involved in granulocyte adhesion and leukocyte extravasation signaling. Plasma and urine proteins could effectively distinguish RCC from control, with good performances (plasma/urine: 92.6%/92.6% specificity, 96.3%/92.6% sensitivity, and an area under the curve of 0.981/0.97). In conclusion, biofluids could not only reflect functional changes in tumor tissue but also reflect changes in the body's immune response. These findings will benefit the understanding of body biomarkers in tumors and the discovery of potential disease biomarkers. Urine ; Proteome ; Liquid biopsy ; Proteomics ; Blood proteins ; Chemistry ; Biomarker ; Pathology ; Cancer ; Biology ; Biochemistry ; Medicine ; Internal medicine ; Gene Advanced Proteomics Techniques and Applications ; Renal cell carcinoma treatment ; Erythropoietin and Anemia Treatment
Authors: Nicola Cinosi | Silvia Portarena | Leen Almadi | Annalisa Berrettini | Mariela Torres | Pierluigi Pierantozzi | Fabíola Villa | Andrea Galletti | Franco Famiani | Daniela Farinelli
Source: Agriculture (article, 2023-01-12)
Field: Chemistry — Analytical Chemistry

Use of Portable Devices and an Innovative and Non-Destructive Index for In-Field Monitoring of Olive Fruit Ripeness A new non-destructive index of absorbance difference (IAD), calculated with different wavelengths as references, near the chlorophyll absorbance peak or that for anthocyanin, has been used to determine the fruit ripening of several fruit species using portable devices such as Standard DA-Meter® and Kiwi-Meter®. In this study, for the first time, the application of the IAD was evaluated on single, intact olives of the Leccino cultivar, as a single non-destructive maturity index to follow ripening in the field, directly on the tree. The IAD determined by both devices was found to be useful for monitoring the olive ripening, however, better performance was achieved with the Kiwi-Meter®. In fact, the IAD determined by this device was more effective at measuring all the studied olive ripening parameters in the calibration and validation datasets with acceptable accuracy. Specifically, better performance of the IDA by Kiwi-Meter® was observed in estimation of the color index (R2 = 0.817, DRMSEC = 0.404 and RPIQ = 3.863 for calibration and R2 = 0.752, DRMSECV = 0.574 and RPIQ = 3.244 for validation), confirming its potential for evaluating the degree of olive ripeness. This novel index has significant applicative possibilities, since it would allow rapid, on-site screening of the best harvesting time to improve the olive production in terms of both yield and quality. Ripeness ; Ripening ; Actinidia deliciosa ; Absorbance ; Horticulture ; Environmental science ; Chemistry ; Biology ; Chromatography Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing in Agriculture
Authors: Elke Debrie | Milan Malfait | Ralf Gabriels | Arthur Declerq | Adriaan Sticker | Lennart Martens | Lieven Clement
Source: Journal of Proteome Research (article, 2023-01-17)
Field: Chemistry — Spectroscopy

Quality Control for the Target Decoy Approach for Peptide Identification Reliable peptide identification is key in mass spectrometry (MS) based proteomics. To this end, the target decoy approach (TDA) has become the cornerstone for extracting a set of reliable peptide-to-spectrum matches (PSMs) that will be used in downstream analysis. Indeed, TDA is now the default method to estimate the false discovery rate (FDR) for a given set of PSMs, and users typically view it as a universal solution for assessing the FDR in the peptide identification step. However, the TDA also relies on a minimal set of assumptions, which are typically never verified in practice. We argue that a violation of these assumptions can lead to poor FDR control, which can be detrimental to any downstream data analysis. We here therefore first clearly spell out these TDA assumptions, and introduce TargetDecoy, a Bioconductor package with all the necessary functionality to control the TDA quality and its underlying assumptions for a given set of PSMs. False discovery rate ; Decoy ; Bioconductor ; Identification (biology) ; Set (abstract data type) ; Computer science ; Downstream (manufacturing) ; Quality (philosophy) ; Key (lock) ; Data mining ; Chemistry ; Engineering ; Biology ; Computer security Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced biosensing and bioanalysis techniques
Authors: James E. Larson | Penelope Perkins‐Veazie | Guoying Ma | Thomas M. Kon
Source: Horticulturae (article, 2023-02-18)
Field: Chemistry — Analytical Chemistry

Quantification and Prediction with Near Infrared Spectroscopy of Carbohydrates throughout Apple Fruit Development Carbohydrates play a key role in apple fruit growth and development. Carbohydrates are needed for cell division/expansion, regulate fruitlet abscission, and influence fruit maturation and quality. Current methods to quantify fruit carbohydrates are labor intensive and expensive. We quantified carbohydrates throughout a growing season in two cultivars and evaluated the use of near infrared spectroscopy (NIR) to predict apple carbohydrate content throughout changes in fruit development. Carbohydrates were quantified with high performance liquid chromatography (HPLC) at five timepoints between early fruitlet growth and harvest in ‘Gala’ and ‘Red Delicious’ apples. NIR spectra was collected for freeze-dried fruit samples using a benchtop near infrared spectrometer. Sorbitol was the major carbohydrate early in the growing season (~40% of total carbohydrates). However, the relative contribution of sorbitol to total carbohydrates rapidly decreased by 59 days after full bloom (&lt;10%). The proportion of fructose to total carbohydrates increased throughout fruit development (40–50%). Three distinct periods of fruit development, early, mid-season, and late, were found over all sampling dates using principal component analysis. The first (PC1) and second (PC2) principal components accounted for 90% of the variation in the data, samples separated among sampling date along PC1. Partial least squares regression was used to build the models by calibrating carbohydrates quantified with HPLC and measured reflectance spectra. The NIR models reliably predicted the content of fructose, glucose, sorbitol, sucrose, starch, and total soluble sugars for both ‘Gala’ and ‘Red Delicious’; r2 ranged from 0.60 to 0.96. These results show that NIR can accurately estimate carbohydrates throughout the growing season and offers an efficient alternative to liquid or gas chromatography. Sucrose ; Sorbitol ; Sugar ; Fructose ; Chemistry ; Growing season ; Malus ; Horticulture ; Carbohydrate ; Partial least squares regression ; Cultivar ; Botany ; Starch ; Food science ; Biology ; Biochemistry Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Plant Physiology and Cultivation Studies
Authors: Tine Claeys | Maxime Menu | Robbin Bouwmeester | Kris Gevaert | Lennart Martens
Source: Journal of Proteome Research (article, 2023-03-24)
Field: Chemistry — Spectroscopy

Machine Learning on Large-Scale Proteomics Data Identifies Tissue and Cell-Type Specific Proteins Using data from 183 public human data sets from PRIDE, a machine learning model was trained to identify tissue and cell-type specific protein patterns. PRIDE projects were searched with ionbot and tissue/cell type annotation was manually added. Data from physiological samples were used to train a Random Forest model on protein abundances to classify samples into tissues and cell types. Subsequently, a one-vs-all classification and feature importance were used to analyze the most discriminating protein abundances per class. Based on protein abundance alone, the model was able to predict tissues with 98% accuracy, and cell types with 99% accuracy. The F-scores describe a clear view on tissue-specific proteins and tissue-specific protein expression patterns. In-depth feature analysis shows slight confusion between physiologically similar tissues, demonstrating the capacity of the algorithm to detect biologically relevant patterns. These results can in turn inform downstream uses, from identification of the tissue of origin of proteins in complex samples such as liquid biopsies, to studying the proteome of tissue-like samples such as organoids and cell lines. Proteome ; Proteomics ; Cell type ; Random forest ; Computational biology ; Biology ; Cell ; Computer science ; Human Protein Atlas ; Artificial intelligence ; Bioinformatics ; Protein expression ; Biochemistry Advanced Proteomics Techniques and Applications ; Gene expression and cancer classification ; Biomedical Text Mining and Ontologies
Authors: Weiwen Sun | Yuan Lin | Yue Huang | Josolyn Chan | Sonia Terrillon | Anton I. Rosenbaum | Kévin Contrepois
Source: Molecular & Cellular Proteomics (article, 2023-05-02)
Field: Chemistry — Spectroscopy

Robust and High-Throughput Analytical Flow Proteomics Analysis of Cynomolgus Monkey and Human Matrices With Zeno SWATH Data-Independent Acquisition Modern mass spectrometers routinely allow deep proteome coverage in a single experiment. These methods are typically operated at nanoflow and microflow regimes, but they often lack throughput and chromatographic robustness, which is critical for large-scale studies. In this context, we have developed, optimized, and benchmarked LC-MS methods combining the robustness and throughput of analytical flow chromatography with the added sensitivity provided by the Zeno trap across a wide range of cynomolgus monkey and human matrices of interest for toxicological studies and clinical biomarker discovery. Sequential Window Acquisition of All Theoretical Fragment Ion Mass Spectra (SWATH) data-independent acquisition (DIA) experiments with Zeno trap activated (Zeno SWATH DIA) provided a clear advantage over conventional SWATH DIA in all sample types tested with improved sensitivity, quantitative robustness, and signal linearity as well as increased protein coverage by up to 9-fold. Using a 10-min gradient chromatography, up to 3300 proteins were identified in tissues at 2 μg peptide load. Importantly, the performance gains with Zeno SWATH translated into better biological pathway representation and improved the ability to identify dysregulated proteins and pathways associated with two metabolic diseases in human plasma. Finally, we demonstrate that this method is highly stable over time with the acquisition of reliable data over the injection of 1000+ samples (14.2 days of uninterrupted acquisition) without the need for human intervention or normalization. Altogether, Zeno SWATH DIA methodology allows fast, sensitive, and robust proteomic workflows using analytical flow and is amenable to large-scale studies. Robustness (evolution) ; Computer science ; Mass spectrometry ; Proteomics ; Proteome ; Biological system ; Biomarker discovery ; Systems biology ; Chemistry ; Computational biology ; Chromatography ; Bioinformatics ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies