jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xichen Lian | Yintao Zhang | Ying Zhou | Xiuna Sun | Shijie Huang | Haibin Dai | Lianyi Han | Feng Zhu
Source: Nucleic Acids Research (article, 2023-10-11)
Field: Chemistry — Spectroscopy

SingPro: a knowledge base providing single-cell proteomic data Single-cell proteomics (SCP) has emerged as a powerful tool for detecting cellular heterogeneity, offering unprecedented insights into biological mechanisms that are masked in bulk cell populations. With the rapid advancements in AI-based time trajectory analysis and cell subpopulation identification, there exists a pressing need for a database that not only provides SCP raw data but also explicitly describes experimental details and protein expression profiles. However, no such database has been available yet. In this study, a database, entitled 'SingPro', specializing in single-cell proteomics was thus developed. It was unique in (a) systematically providing the SCP raw data for both mass spectrometry-based and flow cytometry-based studies and (b) explicitly describing experimental detail for SCP study and expression profile of any studied protein. Anticipating a robust interest from the research community, this database is poised to become an invaluable repository for OMICs-based biomedical studies. Access to SingPro is unrestricted and does not mandate a login at: http://idrblab.org/singpro/. Proteomics ; Biology ; Computational biology ; Identification (biology) ; Database ; Protein expression ; Reference database ; Bioinformatics ; Computer science ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Cell Image Analysis Techniques
Authors: F. W. Ping | Jihong Yang | Xuejian Zhou | Yuan Su | Yanlun Ju | Yulin Fang | Xuebing Bai | Wenzheng Liu
Source: Foods (article, 2023-06-14)
Field: Chemistry — Analytical Chemistry

Quality Assessment and Ripeness Prediction of Table Grapes Using Visible–Near-Infrared Spectroscopy Ripeness significantly affects the commercial values and sales of fruits. In order to monitor the change of grapes' quality parameters during ripening, a rapid and nondestructive method of visible-near-infrared spectral (Vis-NIR) technology was utilized in this study. Firstly, the physicochemical properties of grapes at four different ripening stages were explored. Data evidenced increasing color in redness/greenness (<i>a</i>*) and Chroma (<i>C</i>*) and soluble solids (SSC) content and decreasing values in color of lightness (<i>L</i>*), yellowness/blueness (<i>b</i>*) and Hue angle (<i>h</i>*), hardness, and total acid (TA) content as ripening advanced. Based on these results, spectral prediction models for SSC and TA in grapes were established. Effective wavelengths were selected by the competitive adaptive weighting algorithm (CARS), and six common preprocessing methods were applied to pretreat the spectra data. Partial least squares regression (PLSR) was applied to establish models on the basis of effective wavelengths and full spectra. The predictive PLSR models built with full spectra data and 1st derivative preprocessing provided the best values of performance parameters for both SSC and TA. For SSC, the model showed the coefficients of determination for calibration (RCal2) and prediction (RPre2) set of 0.97 and 0.93, respectively, the root mean square error for calibration set (RMSEC) and prediction set (RMSEP) of 0.62 and 1.27, respectively; and the RPD equal to 4.09. As for TA, the optimum values of RCal2, RPre2, RMSEC, RMSEP and RPD were 0.97, 0.94, 0.88, 1.96 and 4.55, respectively. The results indicated that Vis-NIR spectroscopy is an effective tool for the rapid and non-destructive detection of SSC and TA in grapes. Ripeness ; Partial least squares regression ; Calibration ; Near-infrared spectroscopy ; Mathematics ; Ripening ; Mean squared error ; Chemistry ; Analytical Chemistry (journal) ; Statistics ; Food science ; Optics ; Chromatography ; Physics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Fermentation and Sensory Analysis
Authors: Cleo Demeester | Donnia Robins | Angela Elma Edwina | Jos Tournoy | Patrick Augustijns | Ibrahim Ince | Andreas Lehmann | Maria Vertzoni | Jan Schlender
Source: European Journal of Pharmaceutical Sciences (review, 2023-06-15)
Field: Chemistry — Analytical Chemistry

Physiologically based pharmacokinetic (PBPK) modelling of oral drug absorption in older adults – an AGePOP review The older population consisting of persons aged 65 years or older is the fastest-growing population group and also the major consumer of pharmaceutical products. Due to the heterogenous ageing process, this age group shows high interindividual variability in the dose-exposure-response relationship and, thus, a prediction of drug safety and efficacy is challenging. Although physiologically based pharmacokinetic (PBPK) modelling is a well-established tool to inform and confirm drug dosing strategies during drug development for special population groups, age-related changes in absorption are poorly accounted for in current PBPK models. The purpose of this review is to summarise the current state-of-knowledge in terms of physiological changes with increasing age that can influence the oral absorption of dosage forms. The capacity of common PBPK platforms to incorporate these changes and describe the older population is also discussed, as well as the implications of extrinsic factors such as drug-drug interactions associated with polypharmacy on the model development process. The future potential of this field will rely on addressing the gaps identified in this article, which can subsequently supplement in-vitro and in-vivo data for more robust decision-making on the adequacy of the formulation for use in older adults and inform pharmacotherapy. Physiologically based pharmacokinetic modelling ; Polypharmacy ; Dosing ; Drug ; Pharmacology ; Medicine ; Pharmacokinetics ; Population ; Drug development ; Environmental health Analytical Methods in Pharmaceuticals ; Pharmaceutical studies and practices ; Pharmaceutical Practices and Patient Outcomes
Authors: Renan Falcioni | Werner Camargos Antunes | José Alexandre Melo Demattê | Marcos Rafael Nanni
Source: Plants (article, 2023-06-16)
Field: Chemistry — Analytical Chemistry

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet-visible (UV-VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R<sup>2</sup> values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C<sub>3</sub> and C<sub>4</sub> plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, β-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants. Hyperspectral imaging ; Principal component analysis ; Partial least squares regression ; Remote sensing ; Vegetation (pathology) ; Shortwave ; Ranging ; Coefficient of determination ; Reflectivity ; Near-infrared spectroscopy ; Photochemical Reflectance Index ; Environmental science ; Mathematics ; Leaf area index ; Agronomy ; Computer science ; Normalized Difference Vegetation Index ; Statistics ; Optics ; Biology ; Radiative transfer Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Evia Zunita D. Pratiwi | Muhammad Fahri Reza Pahlawan | Diah N. Rahmi | Hanim Zuhrotul Amanah | Rudiati Evi Masithoh
Source: Open Agriculture (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible–shortwave near-infrared spectroscopy Abstract Visible–shortwave near-infrared spectroscopy has been used for internal quality measurement, but the optical penetration to the thickness of fruit skin becomes a challenge. This research aimed to develop partial least square regression model for the soluble solid content (SSC) measurement of fruits having various skin thicknesses, namely dragon fruit, tomato, guava, sapodilla, and banana. The spectra of each fruit were taken in a reflectance mode over a wavelength range of 400–1,000 nm. The best models obtained from banana and sapodilla yielded determination coefficient of prediction ( R 2 p ) of 0.88 and 0.90 and root mean square error of prediction (RMSEP) 0.39 and 0.38°Brix, respectively. The banana and sapodilla SSC prediction models should be able to be used carefully in a variety of applications. Tomato and guava had moderately thinner skin but had the lower R 2 p of 0.64 and 0.76 and the RMSEP of 0.17 and 0.26°Brix, respectively. The poorest model was yielded by dragon fruit, which had the thickest skin with the R 2 p of 0.59 and the RMSEP of 0.40°Brix. The model for guava, although having low R 2 p , can still be utilized as a screening criterion and in some other ‘approximate’ applications. However, the SSC prediction model for tomatoes and dragon fruit is not recommended to use and requires additional research. In addition to the effect of skin thickness, other fruit morphological influences the result of this study. Internal structure and seed number influence the reflection optical geometry, which also affects the SSC prediction model. Brix ; Horticulture ; Skin color ; Coefficient of determination ; Shortwave ; Mathematics ; Reflectivity ; Botany ; Chemistry ; Food science ; Biology ; Optics ; Physics ; Statistics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Jason Derks | Nikolai Slavov
Source: Journal of Proteome Research (review, 2023-02-03)
Field: Chemistry — Spectroscopy

Strategies for Increasing the Depth and Throughput of Protein Analysis by plexDIA Accurate protein quantification is key to identifying protein markers, regulatory relationships between proteins, and pathophysiological mechanisms. Realizing this potential requires sensitive and deep protein analysis of a large number of samples. Toward this goal, proteomics throughput can be increased by parallelizing the analysis of both precursors and samples using multiplexed data independent acquisition (DIA) implemented by the plexDIA framework: https://plexDIA.slavovlab.net. Here we demonstrate the improved precisions of retention time estimates within plexDIA and how this enables more accurate protein quantification. plexDIA has demonstrated multiplicative gains in throughput, and these gains may be substantially amplified by improving the multiplexing reagents, data acquisition, and interpretation. We discuss future directions for advancing plexDIA, which include engineering optimized mass-tags for high-plexDIA, introducing isotopologous carriers, and developing algorithms that utilize the regular structures of plexDIA data to improve sensitivity, proteome coverage, and quantitative accuracy. These advances in plexDIA will increase the throughput of functional proteomic assays, including quantifying protein conformations, turnover dynamics, modifications states and activities. The sensitivity of these assays will extend to single-cell analysis, thus enabling functional single-cell protein analysis. Throughput ; Computer science ; Proteomics ; Multiplexing ; Quantitative proteomics ; Proteome ; Computational biology ; Bioinformatics ; Biology ; Telecommunications Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Advanced Biosensing Techniques and Applications
Authors: Se Ik Kim | Suhyun Hwangbo | Kisoon Dan | Hee Seung Kim | Hyun Hoon Chung | Jae‐Weon Kim | Noh Hyun Park | Yong Sang Song | Dohyun Han | Maria Lee
Source: Molecular & Cellular Proteomics (article, 2023-01-17)
Field: Chemistry — Spectroscopy

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry-based proteomics methods. We conducted label-free liquid chromatography-tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073-2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets. Ovarian carcinoma ; Serous fluid ; Biomarker discovery ; Proteomics ; Computational biology ; Oncology ; Internal medicine ; Medicine ; Chemistry ; Biology ; Ovarian cancer ; Biochemistry ; Cancer Advanced Proteomics Techniques and Applications ; Ovarian cancer diagnosis and treatment ; Ferroptosis and cancer prognosis
Authors: Jeff Jones | Elliot J. MacKrell | Ting‐Yu Wang | Brett Lomenick | M. L. Roukes | Tsui‐Fen Chou
Source: BMC Bioinformatics (article, 2023-06-06)
Field: Chemistry — Spectroscopy

Tidyproteomics: an open-source R package and data object for quantitative proteomics post analysis and visualization Tidyproteomics aims to simplify data exploration from multiple platforms, provide control over individual functions and analysis order, and serve as a tool to assemble complex repeatable processing workflows in a logical flow. Datasets in tidyproteomics are easy to work with, have a structure that allows for biological annotations to be added, and come with a framework for developing additional analysis tools. The consistent data structure and accessible analysis and plotting tools also offers a way for researchers to save time on mundane data manipulation tasks. Visualization ; Open source ; Proteomics ; Computer science ; R package ; Computational biology ; DNA microarray ; Data visualization ; Bioinformatics ; Data science ; Biology ; Data mining ; Software ; Programming language ; Genetics Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Metabolomics and Mass Spectrometry Studies
Authors: Ramon Alejandro Gutiérrez-Saucedo | J. Gomez-Lopez | Adrián A. Villanueva-Briseño | Antonio Topete | J. F. A. Soltero | E. Mendizábal | Carlos F. Jasso-Gastinel | Pablo Taboada | Edgar Benjamín Figueroa Ochoa
Source: Polymers (article, 2023-05-10)
Field: Materials Science — Biomaterials

Pluronic F127 and P104 Polymeric Micelles as Efficient Nanocarriers for Loading and Release of Single and Dual Antineoplastic Drugs The potential application of biodegradable and biocompatible polymeric micelles formed by Pluronic F127 and P104 as nanocarriers of the antineoplastic drugs docetaxel (DOCE) and doxorubicin (DOXO) is presented in this work. The release profile was carried out under sink conditions at 37 °C and analyzed using the Higuchi, Korsmeyer-Peppas, and Peppas-Sahlin diffusion models. The cell viability of HeLa cells was evaluated using the proliferation cell counting kit CCK-8 assay. The formed polymeric micelles solubilized significant amounts of DOCE and DOXO, and released them in a sustained manner for 48 h, with a release profile composed of an initial rapid release within the first 12 h followed by a much slower phase the end of the experiments. In addition, the release was faster under acidic conditions. The model that best fit the experimental data was the Korsmeyer-Peppas one and denoted a drug release dominated by Fickian diffusion. When HeLa cells were exposed for 48 h to DOXO and DOCE drugs loaded inside P104 and F127 micelles, they showed lower IC<sub>50</sub> values than those reported by other researchers using polymeric nanoparticles, dendrimers or liposomes as alternative carriers, indicating that a lower drug concentration is needed to decrease cell viability by 50%. Nanocarriers ; Micelle ; Poloxamer ; HeLa ; Chemistry ; Viability assay ; Biophysics ; Chemical engineering ; Polymer ; Drug delivery ; Organic chemistry ; Cell ; Aqueous solution ; Biochemistry ; Copolymer Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Pengyu Chen | Kevin D. Dorfman
Source: Proceedings of the National Academy of Sciences (article, 2023-11-03)
Field: Materials Science — Materials Chemistry

Gaming self-consistent field theory: Generative block polymer phase discovery Block polymers are an attractive platform for uncovering the factors that give rise to self-assembly in soft matter owing to their relatively simple thermodynamic description, as captured in self-consistent field theory (SCFT). SCFT historically has found great success explaining experimental data, allowing one to construct phase diagrams from a set of candidate phases, and there is now strong interest in deploying SCFT as a screening tool to guide experimental design. However, using SCFT for phase discovery leads to a conundrum: How does one discover a new morphology if the set of candidate phases needs to be specified in advance? This long-standing challenge was surmounted by training a deep convolutional generative adversarial network (GAN) with trajectories from converged SCFT solutions, and then deploying the GAN to generate input fields for subsequent SCFT calculations. The power of this approach is demonstrated for network phase formation in neat diblock copolymer melts via SCFT. A training set of only five networks produced 349 candidate phases spanning known and previously unexplored morphologies, including a chiral network. This computational pipeline, constructed here entirely from open-source codes, should find widespread application in block polymer phase discovery and other forms of soft matter. Generative grammar ; Block (permutation group theory) ; Computer science ; Pipeline (software) ; Phase (matter) ; Set (abstract data type) ; Soft matter ; Field (mathematics) ; Construct (python library) ; Phase diagram ; Theoretical computer science ; Artificial intelligence ; Physics ; Chemistry ; Mathematics ; Quantum mechanics ; Geometry Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Vora Hardagna Vatsal | Brajesh Kumar Jha | Tajinder Pal Singh
Source: The European Physical Journal Plus (article, 2023-06-05)
Field: Chemistry — Organic Chemistry

To study the effect of ER flux with buffer on the neuronal calcium Calcium ; SERCA ; Endoplasmic reticulum ; Calcium signaling ; Chemistry ; BAPTA ; Thapsigargin ; Inositol trisphosphate receptor ; Flux (metallurgy) ; Biophysics ; Calcium in biology ; Voltage-dependent calcium channel ; Receptor ; Biochemistry ; Inositol ; Biology Free Radicals and Antioxidants ; Cholinesterase and Neurodegenerative Diseases ; Computational Drug Discovery Methods
Authors: Ankit Negi | Alejandro Rodriguez | Xuanyi Zhang | Andrew H. Comstock | Cong Yang | Dali Sun | Xiaoning Jiang | Divine P. Kumah | Ming Hu | Jùn Líu
Source: Advanced Science (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Thickness‐Dependent Thermal Conductivity and Phonon Mean Free Path Distribution in Single‐Crystalline Barium Titanate Nanosized perovskite ferroelectrics are widely employed in several electromechanical, photonics, and thermoelectric applications. Scaling of ferroelectric materials entails a severe reduction in the lattice (phonon) thermal conductivity, particularly at sub-100 nm length scales. Such thermal conductivity reduction can be accurately predicted using the information of phonon mean free path (MFP) distribution. The current understanding of phonon MFP distribution in perovskite ferroelectrics is still inconclusive despite the critical thermal management implications. Here, high-quality single-crystalline barium titanate (BTO) thin films, a representative perovskite ferroelectric material, are grown at several thicknesses. Using experimental thermal conductivity measurements and first-principles based modeling (including four-phonon scattering), the phonon MFP distribution is determined in BTO. The simulation results agree with the measured thickness-dependent thermal conductivity. The results show that the phonons with sub-100 nm MFP dominate the thermal transport in BTO, and phonons with MFP exceeding 10 nm contribute ≈35% to the total thermal conductivity, in significant contrast to previously published experimental results. The experimentally validated phonon MFP distribution is consistent with the theoretical predictions of other complex crystals with strong anharmonicity. This work paves the way for thermal management in nanostructured and ferroelectric-domain-engineered systems for oxide perovskite-based functional materials. Phonon ; Thermal conductivity ; Materials science ; Condensed matter physics ; Ferroelectricity ; Mean free path ; Barium titanate ; Perovskite (structure) ; Thermoelectric effect ; Anharmonicity ; Thermoelectric materials ; Phonon scattering ; Scattering ; Optoelectronics ; Optics ; Thermodynamics ; Dielectric ; Physics ; Chemistry ; Composite material ; Crystallography Ferroelectric and Piezoelectric Materials ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices