High-throughput Experimental Methods Research Articles

Impact of Binder Selection on Functional Properties of Polymer Nanocomposite Featured with Metal Oxide Nanoparticle

The effectiveness of nanocomposite materials depends on the accuracy of the binder selection process. This process is crucial for ensuring compatibility, better adhesive behaviour, and stability during the synthesis of polymer composites with metal nanoparticles. An investigation was conducted to analyze the accuracy and efficiency of binder materials on metal nanoparticle (oxide) featured polymer composites. The investigation utilized Advanced Spectroscopic Computational Optimization Analysis (ASCOA) and high-throughput experimental methods to identify and optimize binders using computational modelling (ACM). The processes driving binder-nanoparticle interactions were unravelled by combining advanced characterization methods such as Transition Metal Oxide-based Materials (TMO-M), non-hydrolyzable sialic acid (N-HSIA), and oxygen reduction reaction process (ORR) investigations. In-depth simulation analyses were performed to verify the effectiveness of the proposed methods and gain further understanding. These simulations investigated alternative binding arrangements to maximize material performance while considering sustainability and economic factors. The results demonstrated how binder choice affects material properties, which can be valuable for designing better nanocomposite materials for specific purposes. Additionally, the ASCOA and ACM analyses revealed an accuracy ratio of more than 90% and efficiency ratios of 86% and 84% for ASCOA and ACM, respectively.

Quantitative Characterization of RCA-Based DNA Hydrogels - Towards Rational Materials Design.

DNA hydrogels hold significant promise for biomedical applications and can be synthesized through enzymatic Rolling Circle Amplification (RCA). Due to the exploratory nature of this emerging field, standardized RCA protocols specifying the impact of reaction parameters are currently lacking. This study varied template sequences and reagent concentrations, evaluating RCA synthesis efficiency and hydrogel mechanical properties through quantitative PCR (qPCR) and indentation measurements, respectively. Primer concentration and stabilizing additives showed minimal impact on RCA efficiency, while changes in polymerase and nucleotide concentrations had a stronger effect. Concentration of the circular template exerted the greatest influence on RCA productivity. An exponential correlation between hydrogel viscosity and DNA amplicon concentration was observed, with nucleobase sequence significantly affecting both amplification efficiency and material properties, particularly through secondary structures. This study suggests that combining high-throughput experimental methods with structural folding prediction offers a viable approach for systematically establishing structure-property relationships, aiding the rational design of DNA hydrogel material systems.

Electrical current-assisted reactive crucible melting technique: Case study of the Fe-Sn system

Modern materials science relies heavily on the advancement of combinatorial high-throughput experimental and computational methods to discover new advanced materials. Among these methods, the reactive crucible melting (RCM) technique stands out as particularly promising. However, a significant challenge in RCM combinatorial analysis is the occurrence of "missing" phases. In this study, we successfully addressed this limitation through the application of electrical current stressing. Furthermore, we investigate the influence of electric current stressing on diffusion rates and the phase formation processes within the reactive crucible. We employed Comsol Multiphysics modeling to analyze the experimental results in the framework of electromigration theory, focusing on the distribution of electrical field lines, temperature gradients, and current density values. Additionally, through the integration of finite element calculations with analysis of Electron Backscatter Diffraction (EBSD) and Magneto-Optical Kerr Effect (MOKE) data, we estimated the gradient of internal stresses along the boundaries of the crucible's reaction volume induced by the diffusion of electrically-induced vacancies and adatoms. Due to modest values of the current density in our experiment and the disparity between the rates of crucible body dissolution and vacancy diffusion, no compressive or tensile stresses were observed. Nonetheless, we propose that the developed electrical current-assisted reactive crucible melting technique holds promise for synthesizing metastable phases existing under high positive or negative pressures.

The screening of reasonable copper and phosphorus content in weathering steel by a high-throughput experimental method

The effect of coupling of copper and phosphorus in steel on atmospheric corrosion behavior was investigated using high-throughput experimental methods. Wire beam electrodes and plates with discrete distribution of composition gradients were prepared through a selective laser melting method. By combining array electrode scanning tests and EPMA, the electrochemical properties and the relationship between different copper and phosphorus contents were rapidly determined. Plenty of data obtained were analyzed according to relevant standards and screened to identify compositional ranges corresponding with the relevant compositions of existing grades of steel. To further analyze the effect of copper and phosphorus on the corrosion behavior, dry/wet cyclic corrosion test were performed using plates. A combination of XRD, FTIR, SEM, EDS, and EPMA were used to analyze the corrosion behavior. It was observed that both copper and phosphorus promoted the formation of amorphous substances, supporting the proposed mechanism by previous researchers. Furthermore, in the absence of manganese interference, copper could accumulate at the metal/rust layer interface, rendering the rust layer cation-selective and effectively inhibiting the deep penetration of chloride ions. Moving forward, we expect to develop this high-throughput experiments to investigate the coupling effects of other elements on corrosion behavior.

High-throughput experimental method for measuring fatigue crack growth rate curve of soft materials

The fatigue properties of soft materials, including fatigue crack initiation and propagation, fatigue life and threshold, have been a significant concern in the past few decades. Measuring the fatigue crack growth rate (FCGR) curve is a standard method to characterize fatigue fracture and has been extensively studied in the literature. Conventional methods employing a pure shear configuration measure only one data point on the FCGR curve per test run. Thus, obtaining the whole FCGR curve with numerous data points becomes excessively time-consuming. This work proposes a high-throughput experimental method for measuring the FCGR curve of soft materials. We prepare ten thermoplastic polyurethane (TPU) specimens in single edge notch tensile configuration with different precut crack lengths, use a mechanical mechanism to apply the same cyclic loads to the ten specimens simultaneously, and identify the crack growth of each specimen by image processing. The proposed high-throughput experimental method enables the measurement of over 90 data points on the FCGR curve in a single test run, facilitating the acquisition of the entire curve with just two runs. The obtained FCGR curve exhibits a linear relationship within its middle region on double logarithmic coordinates. The proposed method is further applied to study the effect of temperature on the FCGR curve of TPU specimens. The current experimental setup can be further optimized to accommodate a wider range of soft materials with diverse mechanical properties and specific loading conditions.

Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity in cis-regulatory elements.

Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ∼25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. We introduce a structure-based learning approach to predict the binding preferences of TFs and the automated modelling of TF regulatory complexes. We show the advantage of using our approach over the classical nearest-neighbor prediction in the limits of remote homology. Starting from a TF sequence or structure, we predict binding preferences in the form of motifs that are then used to scan a DNA sequence for occurrences. The best matches are either profiled with a binding score or collected for their subsequent modeling into a higher-order regulatory complex with DNA. Co-operativity is modelled by: (i) the co-localization of TFs and (ii) the structural modeling of protein-protein interactions between TFs and with co-factors. We have applied our approach to automatically model the interferon-β enhanceosome and the pioneering complexes of OCT4, SOX2 (or SOX11) and KLF4 with a nucleosome, which are compared with the experimentally known structures.

Exploring NiCrAlYSiTa multicomponent coatings: Combining high-throughput synthesis and CALPHAD modeling

Multicomponent alloys pose great challenges for efficiently screening desired composition. Here, we combined high-throughput magnetron-sputtering experiments and CALculation of PHAse Diagrams (CALPHAD) methods to screen NiCrAlYSiTa coatings. The compositionally graded coatings showed increased amorphization with higher Si content, indicated by the steepen T0 curves and enlarged amorphous formation region. For the crystallized coatings, the phase structure transformed from Al- and Cr-rich bcc region to Ni-rich fcc, consistent with our calculated phase diagrams. Owing to the high cohesion energy and strong atomic bonding induced by large atomic radius difference and negative mixing enthalpy, the amorphous coatings exhibited superior hardness (25.8 GPa) and elastic modulus (415.8 GPa) compared to the reported multicomponent coatings. By combining high-throughput experimental and theoretical methods, this work pioneers an integrated framework for rapid development of new multicomponent alloy coatings.

The diversification of methods for studying cell-cell interactions and communication.

No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.

Identification of Antibody-Mediated Hydrolysis Sites of Oligopeptides Corresponding to the SARS-CoV-2 S-Protein by MALDI-TOF Mass Spectrometry.

Antibodies recognizing RBD and the S-protein have been previously demonstrated to be formed in humans after SARS-CoV-2 infection and vaccination with the Sputnik V adenovirus vaccine. These antibodies were found to be active when hydrolyzing FITC-labeled oligopeptides corresponding to linear epitopes of the S-protein. The thin-layer chromatography method allows the relative accumulation of the reaction product to be estimated but cannot identify hydrolysis sites. This study used the MALDI-TOF MS method to establish oligopeptide hydrolysis sites. Using the MALDI-TOF MS method in combination with the analysis of known hydrolysis sites characteristic of canonical proteases allowed us to establish the unique hydrolysis sites inherent only to catalytically active antibodies. We have discovered two 12-mer oligopeptides to have six hydrolysis sites equally distributed throughout the oligopeptide. The other three oligopeptides were found to have two to three closely spaced hydrolysis sites. In contrast to trypsin and chymotrypsin proteases, the catalytically active antibodies of COVID-19 patients have their peptide bond hydrolyzed mainly after proline, threonine, glycine, or serine residues. Here, we propose a new high-throughput experimental method for analyzing the proteolytic activity of natural antibodies produced in viral pathology.

WVDL: weighted voting deep learning model for predicting RNA-protein binding sites.

RNA-binding proteins are important for the process of cell life activities. High-throughput technique experimental method to discover RNA-protein binding sites is time-consuming and expensive. Deep learning is an effective theory for predicting RNA-protein binding sites. Using weighted voting method to integrate multiple basic classifier models can improve model performance. Thus, in our study, we propose a weighted voting deep learning model (WVDL), which uses weighted voting method to combine convolutional neural network (CNN), long short term memory network (LSTM) and residual network (ResNet). First, the final forecast result of WVDL outperforms the basic classifier models and other ensemble strategies. Second, WVDL can extract more effective features by using weighted voting to find the best weighted combination. And, the CNN model also can draw the predicted motif pictures. Third, WVDL gets a competitive experiment result on public RBP-24 datasets comparing with other state-of-the-art methods. The source code of our proposed WVDL can be found in https://github.com/biomg/WVDL.

Investigating phase separation properties of chromatin-associated proteins using gradient elution of 1,6-hexanediol

BackgroundChromatin-associated phase separation proteins establish various biomolecular condensates via liquid–liquid phase separation (LLPS), which regulates vital biological processes spatially and temporally. However, the widely used methods to characterize phase separation proteins are still based on low-throughput experiments, which consume time and could not be used to explore protein LLPS properties in bulk.ResultsBy combining gradient 1,6-hexanediol (1,6-HD) elution and quantitative proteomics, we developed chromatin enriching hexanediol separation coupled with liquid chromatography-mass spectrometry (CHS-MS) to explore the LLPS properties of different chromatin-associated proteins (CAPs). First, we found that CAPs were enriched more effectively in the 1,6-HD treatment group than in the isotonic solution treatment group. Further analysis showed that the 1,6-HD treatment group could effectively enrich CAPs prone to LLPS. Finally, we compared the representative proteins eluted by different gradients of 1,6-HD and found that the representative proteins of the 2% 1,6-HD treatment group had the highest percentage of IDRs and LCDs, whereas the 10% 1,6-HD treatment group had the opposite trend.ConclusionThis study provides a convenient high-throughput experimental method called CHS-MS. This method can efficiently enrich proteins prone to LLPS and can be extended to explore LLPS properties of CAPs in different biological systems.

Asphaltene Inhibitor Screening of Next Generation: Automated High-Throughput Experimentation Method

Asphaltene deposition has been a critical flow assurance challenge, and chemical treatment with continuous asphaltene inhibitor injection has been the go-to approach for operators to preemptively tackle this challenge. Development of the right inhibitor chemistry and dosage is challenging and critical. A smart, automated process for screening numerous asphaltene inhibitor formulations based on the design of experiment (DoE) and high-throughput experimentation (HTE) method has been developed and is discussed in this work. Asphaltene inhibitor formulations with distinct base chemistries, boosters, and solvent packages were blended in various ratios to encompass a large chemical and formulation space with the automated blending station of the HTE setup. These formulations were then dosed into a multitude of crude oil samples produced from different regions of the world at constant dosages to represent a uniform and comparable test fluid matrix. The dispersion state of asphaltene clusters within dosed oil samples, in their native state, was then evaluated using the HTE-asphaltene differential aggregation probe test (ADAPT) techniques to generate thousands of performance data points. Furthermore, the efficiency of top-performing formulations was then cross-validated using standard optical transmittance and deposition-based techniques to gauge performance. The four main pillars of the HTE technique are automation, miniaturization, parallelization, and computational design. Each of these pillars contributes to enhancing the current test method into a more accurate, agile, and quicker technique. Through automation, the overall accuracy of formulation blending as well as performance efficiency measurement is increased. With miniaturization, the resource consumption of raw materials and more importantly crude oil is considerably reduced. For this work, each formulation required less than 300 µL of oil per measurement. Parallelization resulted in completing the test evaluation at a rate of 600 evaluations every 4 hours. These results can then be analyzed through the computational design and analytics software to identify the top-performing formulations along with predicting the optimal chemical formulation space that can lead to maximum performance efficiency. The selected formulations were subsequently blended on a bigger laboratory scale, and their efficacy was cross-checked with both dispersion- (optical transmittance and thermoelectric methods) and deposition-based (flow loop setup) techniques against a benchmark asphaltene inhibitor product, known to work successfully in many fields having asphaltene instability related issues. Through this process, several new asphaltene inhibitor formulations were discovered that outperformed the benchmark blend. Automated HTE technology provides a new dimension to asphaltene inhibitor development work that can carry out performance evaluation of numerous formulations at tremendous speed and minimum crude oil volume. With this technique, one can quickly adapt to the changing requirements of asphaltene inhibitor and its dosage with varying crude oil composition, gas/oil ratio, water cut, or any operational change affecting overall asphaltene stability.

Deformation mechanism diagram and deformation instability of a Ti-5Al-5Mo-5V-1Cr-1Fe titanium alloy during the hot compression

Controlling the hot deformation mechanism of the titanium alloy and avoiding deformation instability are the key points in the hot forging of the titanium alloy. A high throughput experimental method was used to establish a deformation mechanism map efficiently. A continuously varying strain distribution from 0.3 to 1.4 was obtained by the deformation of the double-cone specimens. At the temperature of 910 °C and the strain rate of 1.37 s−1, Combining the analysis of metallography and strain-stress curves, the deformation mechanisms in different regions of the double-cone specimens were divided. Dynamic recovery (DRV) and dynamic recrystallization (DRX) work together in the low strain region (＜0.62), and DRX gradually becomes the dominant deformation mechanism in the medium strain region (0.62–1.02). In the large strain region (1.02–1.4), amounts of deformation bands (DBs) formed. Electron back scatter diffraction (EBSD) technique was used for the microstructure observation and the analysis of deformation mechanism. The results demonstrate that with an increase in strain, the recrystallization fraction increased from 83.8% to 96.3%. Combining the several results of the double-cone specimens, the hot deformation mechanism diagram with a temperature range of 850–930 °C, and a strain range of 0.3–1.4 was established. When the strain reaches a certain degree, it is difficult to further form new recrystallized grains at the grain boundaries. The formation of recrystallized grains within the deformation band has been characterized using transmission electron microscopy (TEM). Under the action of shear force, fine recrystallized grains form within the elongated grains. The concentrated fine recrystallized grains aggravate the microstructural nonuniformity of the Ti-55511 titanium alloy in this area, which finally leads to the deformation instability.

BinderSpace: A package for sequence space analyses for datasets of affinity-selected oligonucleotides and peptide-based molecules.

Discovery of target-binding molecules, such as aptamers and peptides, is usually performed with the use of high-throughput experimental screening methods. These methods typically generate large datasets of sequences of target-binding molecules, which can be enriched with high affinity binders. However, the identification of the highest affinity binders from these large datasets often requires additional low-throughput experiments or other approaches. Bioinformatics-based analyses could be helpful to better understand these large datasets and identify the parts of the sequence space enriched with high affinity binders. BinderSpace is an open-source Python package that performs motif analysis, sequence space visualization, clustering analyses, and sequence extraction from clusters of interest. The motif analysis, resulting in text-based and visual output of motifs, can also provide heat maps of previously measured user-defined functional properties for all the motif-containing molecules. Users can also run principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) analyses on whole datasets and on motif-related subsets of the data. Functionally important sequences can also be highlighted in the resulting PCA and t-SNE maps. If points (sequences) in two-dimensional maps in PCA or t-SNE space form clusters, users can perform clustering analyses on their data, and extract sequences from clusters of interest. We demonstrate the use of BinderSpace on a dataset of oligonucleotides binding to single-wall carbon nanotubes in the presence and absence of a bioanalyte, and on a dataset of cyclic peptidomimetics binding to bovine carbonic anhydrase protein. BinderSpace is openly accessible to the public via the GitHub website: https://github.com/vukoviclab/BinderSpace.

Towards standardized polymer solubility measurements using a parallel crystallizer

Recently developed machine learning approaches to understanding material properties provide new opportunities for studying and developing new knowledge about polymers. The vast application of polymers and their sensitivity to many parameters like molecular weight, temperature, morphology, etc. requires developing high throughput experimentation methods that lead to high quality data for machine learning predictions. Although polymer informatics has significantly improved in recent years using artificial intelligence and machine learning techniques, a significant hurdle in the polymer informatics area is the lack of complete and sufficiently detailed databases. One example where this is a challenge is in developing a dataset for polymer solubility and dissolution, where the classification of “soluble” or “insoluble” is sensitive to molecular weight, concentration, temperature, heating/cooling rate, and other factors that are not always reported or well-controlled. To overcome the challenges with the current dataset on polymer solubility, in this work, we will focus on applying turbidity measurements with precise temperature control using a Crystal16 parallel crystallizer. With this method, the experiments are possible to do in reproducible and time-effective ways with minimal human error while also collecting time-resolved dissolution data continuously throughout the experiment. In this work, we analyze the effect of experimental parameters on measured turbidity for representative polymer/solvent pairs and identify key parameters to control for producing accurate and high quality data for different types of polymers. This approach can lead to detailed results in a controllable manner and will allow researchers to develop information-rich data sets for predicting the polymer solubility using machine learning techniques.

Functional brain networks reflect spatial and temporal autocorrelation.

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler, low-dimensional statistics is largely unknown. To explore this question, we examined resting-state functional magnetic resonance imaging (rs-fMRI) data using complex topology measures from network neuroscience. Here we show that spatial and temporal autocorrelation are reliable statistics that explain numerous measures of network topology. Surrogate time series with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely used complexity measures may help link them to neurobiology.

Taq-Polymerase Stop Assay to Determine Target Selectivity of G4 Ligands in Native Promoter Sequences of MYC, TERT, and KIT Oncogenes.

Computational and high-throughput experimental methods predict thousands of potential quadruplex sequences (PQSs) in the human genome. Often these PQSs contain more than four G-runs, which introduce additional uncertainty into the conformational polymorphism of the G4 DNA. G4-specific ligands, which are currently being actively developed as potential anticancer agents or tools for studying G4 structures in genomes, may preferentially bind to specific G4 structures over the others that can be potentially formed in the extended G-rich genomic region. We propose a simple technique that identifies the sequences that tend to form G4 in the presence of potassium ions or a specific ligand. Thermostable DNA Taq-polymerase stop assay can detect the preferential position of the G4 -ligand binging within a long PQS-rich genomic DNA fragment. This technique was tested for four G4 binders PDS, PhenDC3, Braco-19, and TMPyP4 at three promoter sequences of MYC, KIT, and TERT that contain several PQSs each. We demonstrate that the intensity of polymerase pausing reveals the preferential binding of a ligand to particular G4 structures within the promoter. However, the strength of the polymerase stop at a specific site does not always correlate with the ligand-induced thermodynamic stabilization of the corresponding G4 structure.

Protein phosphorylation database and prediction tools.

Protein phosphorylation, one of the main protein post-translational modifications, is required for regulating various life activities. Kinases and phosphatases that regulate protein phosphorylation in humans have been targeted to treat various diseases, particularly cancer. High-throughput experimental methods to discover protein phosphosites are laborious and time-consuming. The burgeoning databases and predictors provide essential infrastructure to the research community. To date, >60 publicly available phosphorylation databases and predictors each have been developed. In this review, we have comprehensively summarized the status and applicability of major online phosphorylation databases and predictors, thereby helping researchers rapidly select tools that are most suitable for their projects. Moreover, the organizational strategies and limitations of these databases and predictors have been highlighted, which may facilitate the development of better protein phosphorylation predictors in silico.

A novel method for predicting DNA N4-methylcytosine sites based on deep forest algorithm.

N4-methyladenosine (4mC) methylation is an essential epigenetic modification of deoxyribonucleic acid (DNA) that plays a key role in many biological processes such as gene expression, gene replication and transcriptional regulation. Genome-wide identification and analysis of the 4mC sites can better reveal the epigenetic mechanisms that regulate various biological processes. Although some high-throughput genomic experimental methods can effectively facilitate the identification in a genome-wide scale, they are still too expensive and laborious for routine use. Computational methods can compensate for these disadvantages, but they still leave much room for performance improvement. In this study, we develop a non-NN-style deep learning-based approach for accurately predicting 4mC sites from genomic DNA sequence. We generate various informative features represented sequence fragments around 4mC sites, and subsequently implement them into a deep forest (DF) model. After training the deep model using 10-fold cross-validation, the overall accuracies of 85.0%, 90.0%, and 87.8% were achieved for three representative model organisms, A. thaliana, C. elegans, and D. melanogaster, respectively. In addition, extensive experiment results show that our proposed approach outperforms other existing state-of-the-art predictors in the 4mC identification. Our approach stands for the first DF-based algorithm for the prediction of 4mC sites, providing a novel idea in this field.

Static Globularization Behavior and Artificial Neural Network Modeling during Post-Annealing of Wedge-Shaped Hot-Rolled Ti-55511 Alloy.

The globularization of the lamellar α phase by thermomechanical processing and subsequent annealing contributes to achieving the well-balanced strength and plasticity of titanium alloys. A high-throughput experimental method, wedge-shaped hot-rolling, was designed to obtain samples with gradient true strain distribution of 0~1.10. The samples with gradient strain distribution were annealed to obtain the gradient distribution of globularized α phase, which could rapidly assess the globularization fraction of α phase under different conditions. The static globularization behavior under various parameters was systematically studied. The applied prestrain provided the necessary driving force for static globularization during annealing. The substructure evolution and the boundary splitting occurred mainly at the early stage of annealing. The termination migration and the Ostwald ripening were dominant in the prolonged annealing. A backpropagation artificial neural network (BP-ANN) model for static globularization was developed, which coupled the factors of prestrain, annealing temperature, and annealing time. The average absolute relative errors (AARE) for the training and validation set are 3.17% and 3.22%, respectively. Further sensitivity analysis of the factors shows that the order of relative importance for static globularization is annealing temperature, prestrain and annealing time. The developed BP-ANN can precisely predict the static globularization kinetic curves without overfitting.