Experimental Data Research Articles

Unveiling the Cleavage Mechanism of an RNA Model Compound on the whole pH Scale: Computations Meet Experiments in the Determination of Reaction Rates.

The knowledge of the mechanism of reactions occurring in solution is a primary research line both in the context of theoretical-computational chemistry and in the field of organic and bio-organic chemistry. Given the importance of the hydrolysis of nucleic acids in life-related phenomena, here we present a combined experimental and computational study on the cleavage of an RNA model compound. This phosphodiester features a cleavage rate strictly dependent on the pH with three different dependence domains. Such experimental evidence, highlighted by an in-depth kinetic investigation, unequivocally suggests a change in the reaction mechanism along the pH scale. In order to interpret the data and to explain the experimental behavior, we have applied a theoretical-computational procedure, involving a hybrid quantum/classical approach, able to model chemical reactions in complex environments, i. e. in solution. This study turns out to quantitatively reproduce the experimental data with accuracy and, in addition, provides useful mechanistic insight into the transesterification process of the investigated compound. The study indicates that the cleavage can occur through an , an , and a mechanism depending on the pH values.

Near-infrared spectroscopy combined with deep convolutional generative adversarial network for prediction of component content in melt-cast explosive

Rapid and nondestructive prediction of component content is the key to improve industrial production efficiency. However, limited data sets also result in low generalization capabilities of the model, and it is time-consuming to obtain a large amount of content reference values and costly. Here, near infrared (NIR) spectroscopy technique combined with deep convolutional generated countermeasure network (DCGAN) was used to predict the trinitrotoluene (TNT) content of the melt-cast explosive. DCGAN was used to simultaneously extend its spectral data and content data. After several iterations, fake data were produced, which was very similar to the experimental data. The partial least squares (PLS) regression model was established and the performance was compared before and after data enhancement. The results showed that this method not only improved the performance of regression model, but also solved the problem of requiring large number of training data.

Photovoltaic module temperature prediction using various machine learning algorithms: Performance evaluation

This paper presents data-driven models for photovoltaic module temperature prediction and analyzes the relation and effects of ambient conditions to module temperature. A total of 12 different machine learning and regression algorithms are implemented, with a large experimental dataset of 345,600 × 7. Prior to implementing those algorithms, data preprocessing is performed to prepare the datasets and determine the informative attributes for the models. Using PCA with module temperature as target to predict, the selected features for models' inputs were determined to be ambient temperature, solar radiation, wind speed, and relative humidity, and each algorithm is cross-validated and tuned with optimal performance parameters. Results show that while relative humidity is more likely to introduce less information to the model, other aforementioned features are the important parameters to predict the module temperature. While for linear modeling, LASSO algorithm provided the best performance, the ANN model demonstrated the best overall results as it produced the most accurate predictions with lowest errors. A similar performance is attained by the proposed non-linear model, KRR and Gradient Boosting algorithm, with a slight advantage to the KRR model. Furthermore, in comparison to experimental data, the ANN model and the proposed non-linear model provided an R2 values of 0.986 and 0.981, with a MAE of 0.982 and 1.476, and MSE of 2.181and 3.464, respectively. Moreover, the proposed model supplied accurate results when compared to models from literature in an out-of-sample testing, it also proven to be robust and accurate when used to predict the PV power output.

An Instruction Inflation Analyzing Framework for Dynamic Binary Translators

Dynamic binary translators (DBTs) are widely used to migrate applications between different instruction set architectures (ISAs). Despite extensive research to improve DBT performance, noticeable overhead remains, preventing near-native performance, especially when translating from complex instruction set computer (CISC) to reduced instruction set computer (RISC). For computational workloads, the main overhead stems from translated code quality. Experimental data show that state-of-the-art DBT products have dynamic code inflation of at least 1.46. This indicates that on average, more than 1.46 host instructions are needed to emulate one guest instruction. Worse, inflation closely correlates with translated code quality. However, the detailed sources of instruction inflation remain unclear. To understand the sources of inflation, we present Deflater , an instruction inflation analysis framework comprising a mathematical model, a collection of black-box unit tests called BenchMIAOes , and a trace-based simulator called InflatSim . The mathematical model calculates overall inflation based on the inflation of individual instructions and translation block optimizations. BenchMIAOes extract model parameters from DBTs without accessing DBT source code. InflatSim implements the model and uses the extracted parameters from BenchMIAOes to simulate a given DBT’s behavior. Deflater is a valuable tool to guide DBT analysis and improvement. Using Deflater, we simulated inflation for three state-of-the-art CISC-to-RISC DBTs: ExaGear, Rosetta2, and LATX, with inflation errors of 5.63%, 5.15%, and 3.44%, respectively for SPEC CPU 2017, gaining insights into these commercial DBTs. Deflater also efficiently models inflation for the open source DBT QEMU and suggests optimizations that can substantially reduce inflation. Implementing the suggested optimizations confirms Deflater’s effective guidance, with 4.65% inflation error, and gains 5.47x performance improvement.

Efficient end-to-end simulation of time-dependent coherent X-ray scattering experiments.

Physical optics simulations for beamlines and experiments allow users to test experiment feasibility and optimize beamline settings ahead of beam time in order to optimize valuable beam time at synchrotron light sources like NSLS-II. Further, such simulations also help to develop and test experimental data processing methods and software in advance. The Synchrotron Radiation Workshop (SRW) software package supports such complex simulations. We demonstrate how recent developments in SRW significantly improve the efficiency of physical optics simulations, such as end-to-end simulations of time-dependent X-ray photon correlation spectroscopy experiments with partially coherent undulator radiation (UR). The molecular dynamics simulation code LAMMPS was chosen to model the sample: a solution of silica nanoparticles in water at room temperature. Real-space distributions of nanoparticles produced by LAMMPS were imported into SRW and used to simulate scattering patterns of partially coherent hard X-ray UR from such a sample at the detector. The partially coherent UR illuminating the sample can be represented by a set of orthogonal coherent modes obtained by simulation of emission and propagation of this radiation through the coherent hard X-ray (CHX) scattering beamline followed by a coherent-mode decomposition. GPU acceleration is added for several key functions of SRW used in propagation from sample to detector, further improving the speed of the calculations. The accuracy of this simulation is benchmarked by comparison with experimental data.

Abstract 5896: Platform approach to develop antibodies specifically recognizing cancer-associated glycoforms

Abstract Introduction: Recent advances in antibody engineering have created a portfolio of highly potent therapeutic approaches like antibody-drug conjugates (ADCs), radioimmunoconjugates (RITs), chimeric antigen receptors (CARs) or bi-specifics. Due to the increased potency, these drugs are more than ever dependent on very clean targets to prevent side effects. We aim to address this major problem through our approach to develop antibodies with improved tumor specificity and reduced on-target/off-tumor binding utilizing the aberrant O-glycosylation on tumor cells. Altered glycosylation of proteins and lipids is one of the most drastic changes in cancer, giving rise to truncated or highly fucosylated and highly sialylated glycans which are almost absent on normal cells. Thus, developing antibodies against protein/carbohydrate combined epitopes (GlycoTargets) comprising these tumor-specific glycans enables highly potent therapies with reduced off-tumor toxicity. Experimental procedures: A workflow and corresponding database was established to evaluate the O-glycosylation of proteins. The first and most basic criterion is the presence of suitable O-glycosylated peptide stretches in the extracellular domain of a protein based on predictions. For proteins fulfilling this criterion cancer-relevant expression is analyzed in a second step. This is done either using publicly available data or experimentally (immunohistochemistry or protein expression data). During the final major step, the theoretical existence of suitable O-glycosylation is confirmed by our own experimental data. This includes analysis of cellularly expressed proteins by anti-glycan antibodies and mass spectrometric studies. Results summary: Case studies will be presented that highlight our workflow to identify suitable GlycoTargets based on publicly available data analysis and experimental confirmation. Additionally, the subsequent development of antibodies against the identified GlycoTargets will be shown. Conclusion: A standardized process was developed for the data collection and prioritization of potential GlycoTargets. The obtained information is subsequently used for targeted discovery of antibodies that bind to protein/carbohydrate combined glycoepitopes (GlycoTargets) offering increased tumor-specificity compared to simple protein targets. Citation Format: Patrik Kehler, Johanna Gellert, Lisa Kalfhues, Sophie Marinoff, Manon Weis, Andreas Franz, Naomi Kast, Stephanie Gurka, Marianne Morche, Evelyn Hartung, Timo Lischke, Sarah Mayer-Hain, Antje Danielczyk. Platform approach to develop antibodies specifically recognizing cancer-associated glycoforms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5896.

A numerical compass for experiment design in chemical kinetics and molecular property estimation

Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure–activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package KineticCompass.Graphical

Absolute and relative disparity mechanisms revealed by an equivalent noise analysis

The precision of stereopsis and vergence are ultimately limited by internal binocular disparity noise. Here we propose an equivalent noise model with both global and local internal disparity noises to provide a unified explanation of both absolute and relative disparity thresholds. To test this model, we developed a psychophysical procedure to measure the equivalent internal disparity noise by adding external disparity noise to random-Gabor-patch stereograms. We used the method of constant stimuli to measure the minimum and maximum disparity thresholds (Dmin and Dmax) for both absolute and relative disparity. Consistent with previous studies, we found that Dmin thresholds are substantially worse for absolute disparity than for relative disparity. We tested three relative disparity mechanisms: (1) the difference between the monocular separations of targets projecting to the two eyes; (2) the direct measurement of relative disparity; and (3) the difference of absolute disparities of targets. Computing the difference of absolute disparities when detecting relative disparity, Mechanism 3 cancels global noise, resulting in a much lower relative Dmin threshold, and provides a reasonable fit to the experimental data. We also found that the presence of as much as 2400 arcsec of external disparity noise does not appear to affect the Dmax threshold. This observation suggests that Dmax is implicated in a mechanism that disregards the disparity variance of individual items, relying instead on the average disparity across all items, supporting the depth model proposed in our previous study (Ding & Levi, 2021), which posits distinct mechanisms governing Dmin and Dmax thresholds.

In silico agent-based modeling approach to characterize multiple in vitro tuberculosis infection models.

In vitro models of Mycobacterium tuberculosis (Mtb) infection are a valuable tool for examining host-pathogen interactions and screening drugs. With the development of more complex in vitro models, there is a need for tools to help analyze and integrate data from these models. To this end, we introduce an agent-based model (ABM) representation of the interactions between immune cells and bacteria in an in vitro setting. This in silico model was used to simulate both traditional and spheroid cell culture models by changing the movement rules and initial spatial layout of the cells in accordance with the respective in vitro models. The traditional and spheroid simulations were calibrated to published experimental data in a paired manner, by using the same parameters in both simulations. Within the calibrated simulations, heterogeneous outputs are seen for bacterial count and T cell infiltration into the macrophage core of the spheroid. The simulations also predict that equivalent numbers of activated macrophages do not necessarily result in similar bacterial reductions; that host immune responses can control bacterial growth in both spheroid structure dependent and independent manners; that STAT1 activation is the limiting step in macrophage activation in spheroids; and that drug screening and macrophage activation studies could have different outcomes depending on the in vitro culture used. Future model iterations will be guided by the limitations of the current model, specifically which parts of the output space were harder to reach. This ABM can be used to represent more in vitro Mtb infection models due to its flexible structure, thereby accelerating in vitro discoveries.

Theoretical and kinetic study of the hydrogen abstraction reactions of ethyl propionate

Ethyl propionate shows great promise as a biodiesel molecule model. However, the current oxidation kinetic model falls short in accurately predicting its combustion behavior. Therefore, this study employed ab initio quantum chemistry methods to analyze the hydrogen abstraction reactions of ethyl propionate with various reactive radicals, including OH, O, H, CH3, C2H3, C2H5, HO2, CH3O and CH3O2, which play a critical role in refining the oxidation kinetics. Theoretical calculations were conducted to determine the rate constants and branching ratios for these reactions at temperatures ranging from 500 K to 2500 K using transition state theory, quasi-rigid rotor harmonic oscillator model, and tunneling correction. After updating the kinetics of hydrogen abstraction reactions in the oxidation mechanism proposed by Metcalfe et al., the modified model was validated by comparing the simulated mole fraction evolutions of ethyl propionate and major products in a jet-stirred reactor, ignition delay in a closed homogeneous batch reactor, and laminar flame speed with experimental data. The findings indicate that the modified model reliably reproduces the experimental data and significantly improves the prediction accuracy for low-temperature combustion of ethyl propionate when compared to the original model.

Feasibility Study of Experimental Protocol for the Time-Dependent Mechanical Response of Synthetic Tibia.

In this research, an experimental biomechanics construct was developed to reveal the mechanics of distal tibial fracture by submitting synthetic tibiae to cyclic loading, resulting in a combined stress state due to axial compression and bending loads. The synthetic tibia was fixed at the knee but allowed to rotate in the coronal and sagittal planes at the ankle. The first three loading regimes lasted for 4000 cycles/each, and the final until ultimate failure. After 12k±80 cycles, the observed failure patterns closely resembled distal tibial fractures. The collected data during cyclic loading were fitted into a phenomenological model to deduce the time-dependent response of the synthetic tibiae. Images were also collected and analyzed using digital image correlation to deduce the full-field state of strain. The latter revealed that longitudinal strain contours extended in the proximal-distal direction. The transverse strain contours exemplified a medial-to-lateral distribution, attributed to the combined contributions of the Poisson's effect and the flexural deformation from axial and bending components of the applied load, respectively. The experimental construct, full-field characterization, and data analysis approaches can be extended to elucidate the effect of different fixation devices on the overall mechanical behavior of the bone and validate computational models in future research.

Stereochemical and Computational NMR Survey of 1,2,3-Triazoles: in Search of the Original Tauto-Conformers.

The conformational analysis of nine functionalized 1,2,3-triazoles was carried out by the correlation of calculated and experimental high-level nuclear magnetic resonance (NMR) chemical shifts. In solution, the studied triazoles are in exchange dynamic equilibrium caused by their prototropic tautomerism of the NH-proton. The experimentally unresolved NMR signals were assigned for most of the compounds. A more thorough survey was conducted for 4-t-butyl-1,2,3-triazole-5-carbaldehyde oxime. The analysis performed within the framework of the DP4+ formalism completely confirmed the hypothesis of the predominance of the 2H-tautomer. Thus, the methodology for estimating stereochemical structures in the absence of some experimental data allowed the most stable conformations for dynamic systems with different tautomeric ratios to be reliably identified.

Solubility evaluation of palm-based Mono-diacylglycerols (MDAGs) in food grade solvent (hexane, ethanol, acetone, water) using QSPR model approach

Mono-diacylglycerols (MDAGs) are emulsifiers widely used in the food industry. MDAGs based on refined bleached deodorized palm stearin (RBDPS) have not been widely developed. MDAGs have been synthesized using glicerolysis between RBDPS and glycerols. Due to their low solubility in food-grade solvents (hexane, ethanol, acetone, and water), it has been difficult to purify or fractionate MDAGs only by a simple method. This research attempted to develop a model of MDAGs solubility in food-grade solvents and understand the parameters that influence the solubility of MDAGs in food-grade solvents. The process used a combination of experimental data collection and quantitative structure–property relationship (QSPR) models. The QSPR model for MDAGs solubility in a single and binary solvent demonstrated that the descriptors of Δ dipole moment (Δµ), E HOMO, E LUMO, electronic energy, gap energy HOMO-LUMO (ΔE1, ΔE2) softness (S), electrophilicity index (ω), and electron transfer (ΔN) have a significant impact in determining the solubility of MDAGs. Both the single and the binary solvents solubility models can give satisfactory prediction results: the square of the correlation coefficient (R2pred) was 0.933 and 0.948, and the root-mean-square error (RMSE) was 0.075 and 0.031, respectively, for the whole data set. In addition, this paper provided a new and effective method for predicting the solubility of MDAGs in single and binary food-grade solvent systems.

A combined cyclic viscoplasticity and entropy generation approach for modelling fatigue crack growth behavior of a nickel-based superalloy at high temperature

This paper developed an entropy-based approach to estimate the fatigue crack growth (FCG) behavior of GH4169 at high temperature. Firstly, systematic FCG experiments of GH4169 at 650 °C were conducted to obtain the experimental data. Then, a finite element analysis combined with Chaboche viscoplasticity and node release technology was developed to obtain cyclic stress–strain responses and entropy generation fields. Based on thermodynamic analysis, numerous discrete material elements as sub-systems were divided along the crack growth direction in simplified representative 2D middle section. Fracture process zone (FPZ) was defined equal to the size of discrete material elements (ρ) which can be determined by distribution of normal stress perpendicular to the crack plane. Subsequently, an effective cyclic entropy generation related to ρ was defined as the crack driving force. Effective cumulative entropy generation was calculated when crack increment was ρ and the results indicated that effective cumulative entropy generation float within a certain range. The average of all effective cumulative entropy generation values was defined as crack growth entropy. Finally, an entropy-based FCG rate model was established to estimate the FCG behavior of GH4169, and a good correlation between experimental results and model predictions are achieved for all the high temperature FCG tests.

Experimental investigation of thawing behavior of saline soils using resistivity method

Abstract Electrical resistivity method has been widely used to study permafrost and to monitor the process of freezing-thawing. However, a thorough understanding of the mechanism of electrical response during thawing is missing. In this study, we investigated the thawing behavior of saline soils in the temperature range from roughly −10 to 15°C considering the effects of soil type and salinity. A total of nine experiments were performed with three soil types (silica sand, sandy soil, and silt) and three salinities (0.01, 0.1, and 1 S m−1). The results show that resistivity variations with temperature can be divided into three stages. In Stage I, tortuosity and unfrozen water content play major roles in the decrease of resistivity. In Stage Ⅱ, which is an isothermal or near isothermal process, resistivity still decreases slightly due to the thawing of residual ice and pore water movement. In Stage III, ionic mobility plays an important impact on decreasing resistivity. In addition, the isothermal process is found to only occur in silica sand that can be explained by latent heat effect. Exponential and linear models linking temperature with resistivity are used to fit the experimental data in Stages I and III. The fitting parameter in different models shows great correlation with soil type and salinity. Furthermore, unfrozen water content below 0°C is also estimated and uncertainty of estimation is analyzed.

Modelling of reaction kinetics in production of hydroxy carboxylic acids by alkaline degradation of cellulosic waste

The reaction kinetics in alkaline degradation of cellulosic materials to monomeric compounds at elevated temperatures was studied. The target compounds are hydroxy carboxylic acids, preferably glucoisosaccharinic acid (GISA), that are valuable platform chemicals. This methodology, which focuses on depolymerizing cellulosic materials and transforming them into hydroxy carboxylic acids, has not been explored in existing literature. To this end, a rigorous mathematical model was developed that considers phenomena at macroscopic level (transformation of crystalline cellulose into amorphous) and at microscopic level (cleavage of glycosidic bonds). Experimental cellulose degradation data in 10 wt-% NaOH and 20–200 °C was correlated with the model. The agreement between the model results and the experimental data confirmed that the process obeys the proposed reaction pathway. Around 80 % of degradation occurs during the reactor warming up period. Analysis of rate constants indicates that GISA is not degraded into smaller hydroxy acids (SHA) at the temperatures studied. Instead, monosaccharides are converted into SHA as soon as they are produced. Conversion of crystalline cellulose into amorphous form was identified as the rate determining step.

Multiplicity correlation of fast target protons and projectile fragments for the events produced in the interaction of 84Kr nuclei with emulsion nuclei at 1 A GeV

It has become obvious that one crucial aspect in understanding high energy nuclear reactions is the fragmentation of colliding nuclei. The nuclear emulsion is a 4[Formula: see text] detector that makes it simple to identify and quantify the charges of projectile fragments. In this work, we study the multiplicity distribution and angle distributions of single, double charge projectile fragments, fast target protons (gray particle) as well as their correlation for the events produced in the interactions of [Formula: see text] with emulsion nuclei at 1[Formula: see text]A[Formula: see text]GeV. We also study the target-dependent angle distribution of gray particles. The results are compared with other experimental data as per availability. This analysis shows that multiplicity distributions have a remarkable link between the projectile and target fragmentation processes.

THE STUDY OF ELECTRIC QUADRUPOLE TRANSITION (E2) IN 58Ce AND 60Nd NUCLEI

Transition strengths 2w.u. M(E2) for gamma transition from first excited 21+statesto the ground states that produced by pure electric quadrupole emission in even –even nuclei of 58Ce and 60Nd have been calculated as a function of neutron number(N). The life times for 21+ excited states together with the intensities of γ0- transitionsmeasurements are used in calculations. The results thus obtained have shown that;the nuclei with magic neutron number such as 58Ce140 and 60Nd142 have minimumvalue for 2w.u. M(E2) . The reduced transition probabilities B(E2) are alsocalculated and compared with the experimental data and other theoretical models.PACS:- 27.60-j, 23.20-g, 23.20.Ck, 23.20.Gq

Mott–Schottky Analysis of Historical and Archaeological Copper–based Objects

AbstractThe application of Mott–Schottky (M−S) analysis of impedance data to sub–micro samples extracted from the corrosion patina of copper, bronze, and brass archaeological artifacts attached to graphite electrodes is described. The corresponding theoretical approach is developed to account for the contribution of the composition and structure of the corrosion patina and the effect of the bare graphite substrate. Experimental data in contact with 0.10 M Na2SO4 aqueous solution at pH 6.28 are consistent with the p‐type semiconducting nature of the main copper corrosion products, cuprite, and tenorite. The values of the apparent flat band potential and the slope of the M−S plots allow archaeological samples to be grouped according to their M−S parameters (slopes and intercepts). Assuming equivalent conditions of corrosion, the resulting grouping is judged to be dependent on the composition, and/or method of manufacture, and/or age. Studied samples include Renaissance statues from the Hofkirche in Innsbruck and a variety of objects from museums and Archaeological Heritage Office (soprintendenza) in Austria (Bad Aussee, Johanneum Graz, and the Tyrolean State Museums), and Italy (Genoa and San Remo), dating from the Bronze Age to the 18th century. Complementary data are provided by voltammetry of immobilized particles.

Calculation position of the focal point in self-focusing media

Using a laser beam in a self-focusing environment with different power more and less than the critical one, more accurate coordinates of the point with the maximum radiation intensity were obtained. The results of the calculations corresponded to the experimental data obtained by spatial filtering of the angular spectra of forced Raman scattering. The agreement of the results was satisfactory for all beam power levels.