Fast Approach Research Articles

Antibody-Based Array for Tacrolimus Immunosuppressant Monitoring with Planar Plastic Waveguides Activated with an Aminodextran-Lipase Conjugate.

Cyclic olefin copolymers (COC; e.g., Zeonor, Topas, Arton, etc.) are materials with outstanding properties for developing point-of-care systems; however, the lack of functional groups in their native form makes their application challenging. This work evaluates different strategies to functionalize commercially available Zeonor substrates, including oxygen plasma treatment, photochemical grafting, and direct surface amination using an amino dextran-lipase conjugate (ADLC). The modified surfaces were characterized by contact angle measurements, Fourier transform infrared-attenuated total reflection analysis, and fluorescence assays based on evanescent wave excitation. The bioaffinity activation through the ADLC approach results in a fast, simple, and reproducible approach that can be used further to conjugate carboxylated small molecules (e.g., haptens). The usefulness of this approach has been demonstrated by the development of a heterogeneous fluorescence immunoassay to detect tacrolimus (FK506) immunosuppressant drug using an array biosensor platform based on evanescence wave laser excitation and Zeonor-ADLC substrates. Surface modification with ADLC-bearing FK506 provides a 3D layer that efficiently leads to a remarkably low limit of detection (0.02 ng/mL) and IC50 (0.9 ng/mL) together with a wide dynamic range (0.07-11.3 ng/mL).

A Fast Numerical Approach for Investigating Adhesion Strength in Fibrillar Structures: Impact of Buckling and Roughness

This study presents a numerical investigation into the adhesion strength of micro fibrillar structures, incorporating statistical analysis and the effects of excessive pre–load leading to fibril buckling. Fibrils are modeled as soft cylinders using the Euler–Bernoulli beam theory, with buckling conditions described across three distinct states, each affecting the adhesive properties of the fibrils. Iterative simulations analyze how adhesion strength varies with pre–load, roughness, number of fibrils, and the work of adhesion. Roughness is modeled both in fibril heights and in the texture of a rigid counter surface, following a normal distribution with a single variance parameter. Results indicate that roughness and pre–load significantly influence adhesion strength, with excessive pre–load causing substantial buckling and a dramatic reduction in adhesion. This study also finds that adhesion strength decreases exponentially with increasing roughness, in line with theoretical expectations. The findings highlight the importance of buckling and roughness parameters in determining adhesion strength. This study offers valuable insights into the complex adhesive interactions of fibrillar structures, offering a scalable solution for rapid assessment of adhesion in various rough surface and loading scenarios.

Strengthening energy system resilience planning under uncertainty by minimizing regret

This study applies the concept of regret in decision-making under uncertainty to an energy system optimization model to identify optimal robust and stochastic solutions amongst several design options. The approach is demonstrated on the case study of Accra, Ghana, considering uncertainties pertinent to the city, particularly under climate change. The evaluated uncertainty scenarios consider volatile fossil fuel supply, reduced hydropower generation, rising demand due to climate change-driven rural-urban migration and global warming, unplanned power outages due to increasing natural disasters, and currency depreciation. The evaluated systems include Pareto-optimal system solutions typically under consideration by planners, which balance costs and CO2 emissions. The regret performance is evaluated for each system subject to each uncertainty scenario. A near-CO2-minimized system is the optimal robust and stochastic least-regret solution. Two factors drive this result: (1) a diverse technology set, which provides generation and cross-sectoral flexibility for adaptation under uncertainty, and (2) effectively balancing rising investment and operation costs with decreasing unmet demand costs. The demonstrated method provides energy planners and policymakers with a pragmatic, effective and fast approach, which offers new insights into long-term energy system planning to improve resilience under uncertainty, supporting the aims of the United Nations Sustainable Development Goals 7 and 11.

A Novel Approach for Material Handling-Driven Facility Layout

Material handling is a widely used process in manufacturing and is generally considered a non-value-added process. The Dynamic Facility Layout Problem (DFLP) considered in this paper minimizes the total material handling and re-arrangement cost. In this study, an integrated DFLP model with unequal facility areas, assignment of material handling devices (MHD), and flexible bay structure (FBS) is considered, and it is aimed to propose fast solution approaches. Two different solution methods are proposed for the problem, which are the genetic algorithm and the simulated annealing algorithm, respectively. In both methods, a non-linear mathematical model solution was used to calculate the fitness values. Thus, the solutions in the feasible solution space are utilized. The proposed solution approaches were applied to solve four problems published in the literature. The computational experiments have validated the effectiveness of the algorithms and the quality of solutions produced.

Synthesis of lubricant additive for castor oil: A green and fast approach

The realization of industrial applications of plant-based lubricants depends on well-dispersed lubricant additives. Herein, microwave-assisted ball milling directly grafted L-phenylalanine (LP) onto graphene oxide. The reaction does not require a pretreatment step and avoids high temperatures and catalyst involvement. The prepared LP-functionalized graphene oxide (LP-GO) exhibited hydrophobicity with a water contact angle of up to 133° and could maintain stable dispersion in castor oil for more than 80 days. 0.2 wt% of LP-GO improved the thermal conductivity of castor oil by 11.1 % and contributed to the enhancement of tribological performance under three different conditions. In particular, wear and friction decreased by 7.31 % and 11.54 % under the medium speed medium load condition, respectively. The friction decreased by 25.27 % in the low-speed high-load condition, and the wear decreased by 18.65 % under the high-speed low-load condition. The characterization of the tribological interface revealed that the lubrication mechanism of LP-GO was attributed to its protective and repairing effects at the friction interface. The surface functionalization approach in this paper is rapid and green. It can be extended to other functionalizing agents and nanomaterials, facilitating further applications of vegetable oils in tribology.

When less is more - a fast TurboID knock-in approach for high-sensitivity endogenous interactome mapping.

In recent years, proximity labeling has established itself as an unbiased and powerful approach to map the interactome of specific proteins. Although physiological expression of labeling enzymes is beneficial for the mapping of interactors, generation of the desired cell lines remains time-consuming and challenging. Using our established pipeline for rapid generation of C- and N-terminal CRISPR-Cas9 knock-ins (KIs) based on antibiotic selection, we were able to compare the performance of commonly used labeling enzymes when endogenously expressed. Endogenous tagging of the µ subunit of the adaptor protein (AP)-1 complex with TurboID allowed identification of known interactors and cargo proteins that simple overexpression of a labeling enzyme fusion protein could not reveal. We used the KI strategy to compare the interactome of the different AP complexes and clathrin and were able to assemble lists of potential interactors and cargo proteins that are specific for each sorting pathway. Our approach greatly simplifies the execution of proximity labeling experiments for proteins in their native cellular environment and allows going from CRISPR transfection to mass spectrometry analysis and interactome data in just over a month.

Distinct microdiversity of phosphate accumulating organisms (PAOs) between side-stream and conventional enhanced biological phosphorus removal (EBPR) systems with performance implications

Polyphosphate Accumulating Organisms (PAOs) microdiversity is a key factor to elucidate the mechanisms involved in the side-stream enhanced biological phosphorus removal (S2EBPR) systems, which has been shown to improve the process stability over conventional EBPR. However, fast, effective and cost-efficient methods to resolve PAO microdiversity in real-world activate sludge samples is still in absence. In this study, we applied oligotyping analysis following the regular 16S rRNA gene amplicon sequencing standard operation pipeline (SOP) to resolve subgenus-level PAO oligotypes, which cannot be achieved using traditional 16S rRNA sequencing SOP. The identified oligotype profiles of PAO-containing genera Ca. Accumulibacter, Tetrasphaera and Comamonas showed distinguished community-level differences across 12 water resource recovery facilities (WRRFs), which would not be revealed at the genus level. The WRRF-level differences were observed larger than the temporal differences in the same WRRF, indicating intrinsic sub-genus level microdiversity fingerprint between EBPR/S2EBPR systems. The identified oligotypes can be associated with known PAO clades phylogenetically, suggesting that oligotyping can suffice as a fast and cost-efficient approach for PAO microdiversity profiling. In addition, network analysis can be used to identify coexistence patterns between oligotypes with respect to EBPR/S2EBPR configurations and performance, enabling more detailed analysis between EBPR system performance and PAOs microdiversity. Correlation analyses between oligotype profiles and key EBPR performance parameters revealed potential different biological functional traits among these PAO species with P-removal performance implications.

Fast and precise multi-site mutagenesis on linear DNA fragments

The effectiveness and scalability of site-directed mutagenesis are constrained by the limited number of mutations and the intricate cloning process required for isolation of the target sequence. Here, we present a method for precise introduction of multiple non-contiguous mutations. The mutant strands are collected through one specially designed magnetic bead separation in alkaline conditions, efficiently removing their complementary partner strands with the original sequences. In a proof-of-concept test, a green fluorescent protein (GFP) was simultaneously mutated in 1–3 specific amino acids, successfully shifting its fluorescence spectrum. The precise mutation rates for single-, double- and triple-site mutations reached 100%, 76% and 70%, respectively. This multiple non-contiguous mutagenesis method may offer a fast and cost-effective approach for customizable construction of gene library.

Machine learning-based simple and fast approach for the real-time characterization of laser-induced plasma

We report a simple approach to estimate the fast and real-time pulse-to-pulse prediction of plasma parameters, specifically electron temperature and electron number density, using an Artificial Neural network (ANN) in combination with Laser-induced breakdown spectroscopy (LIBS). In a variety of spectroscopic applications, it is essential to have real-time observation of plasma parameters. However, direct measurement of these parameters is challenging and requires complex and time-consuming calculations. Artificial Neural Network (ANN) can be used to model the relation between spectral features from recorded LIBS emission spectra and plasma parameters. The ANN is trained on a suitable preprocessed spectroscopic dataset with corresponding plasma parameters to predict electron temperature and electron number density. The accuracy of Artificial Neural Network (ANN) in predicting the plasma parameters is evaluated, and results are validated with existing conventional methods of calculating plasma parameters, namely the Boltzmann Plot Method for plasma temperature and the Stark Broadening Method for electron number density. The present results show that ANN is an effective method in accurately predicting the plasma parameters directly from the spectral features. The ability to fine-tune plasma in real time enhances control and accuracy in Pulsed Laser Deposition (PLD) process and other plasma coating techniques.

A rapid chemical method for production of xylitol from D-xylose as a renewable feedstock from spent aromatic waste

Xylitol is occurring naturally as a minor constituent in many fruits and vegetables. Commercially, xylitol is produced by chemical methods from D-xylose for application as an alternative sweetener in food and pharmaceutical products. Although biomass-derived xylose can be used as a potential substrate for xylitol production, its prerequisite purification before a catalytic hydrogenation directly impacts the production cost. In addition, the isolation of xylose falls under a routine pretreatment activity during conversion of biomass to platform chemicals. The resulting hydrolysate often exhibits a low concentration of xylose and other monosaccharides. Concentrating the hydrolysate to a desired level for conversion to xylitol under chemical/biological methods increases the expenditure cost. Many times, concentration by heating may cause the degradation of contained sugars in the hydrolysate. Therefore, this study has been carried out to obtain a mixture of C5/6 sugars, predominantly the D-xylose in a solid dry form (∼10 % yield, >98 % purity) directly from acid hydrolysate of spent aromatic waste. Biomass-derived D-xylose was further transformed to xylitol (100 % conversion) by a fast (10 min), efficient, and solvent-free chemical approach using a modified sodium borohydride (NaBH4) catalyst on a wet silica (SiO2) support at RT. All reagents and solvents used in the process were recovered and recycled, leading to the minimal waste generation.

Nanoparticle Assisted Fabric Phase Sorptive Extraction for Azo Dye Determination in the Industrial Sewage.

Currently, one of the significant environmental problems is the presence of azo dye materials in water sources. In this study, for the first time, a fast and sensitive sample preparation approach using nanoparticle-assisted fabric phase sorptive extraction (NFPSE) followed by high-performance liquid chromatography was examined to remove some azo dyes such as methyl red and sunset yellow from aqueous solutions. Primarily, the significance of several parameters affecting NFPSE, such as fabric type, the kind of sorbent, the number of contacts with sol-gel and the time of contact, was investigated. In addition, experiments were performed to determine the effect of different adsorption parameters, such as sample volume, adsorption time, adsorbent value, desorption time, ionic strength and pH. It was found that the calibration curve was linear within two ranges of concentrations (0.05-0.1 and 0.5-15ng/L for methyl red; 0.05-0.5 and 0.5-15ng/L for sunset yellow) with correlation coefficients better than 0.9683. The limit of detection was 0.014ng/L for methyl red and 0.015ng/L for sunset yellow. Repeatability Relative Standard Deviation (RSD) with three replicated experiments was 1.5-10% for methyl red and 2.5-5.8% for sunset yellow. Relative recovery percentages of 88-96% for methyl red and 62-92% for sunset yellow were obtained in the samples. Moreover, the results have shown that acceptable accuracy, precision and linearity make the "fabric phase sorptive extraction" a proper method for the determination of dyes from industrial sewage samples.

Parameter-free super-resolution structured illumination microscopy via a physics-enhanced neural network.

With full-field imaging and high photon efficiency advantages, structured illumination microscopy (SIM) is one of the most potent super-resolution (SR) modalities in bioscience. Regarding SR reconstruction for SIM, spatial domain reconstruction (SDR) has been proven to be faster than traditional frequency domain reconstruction (FDR), facilitating real-time imaging of live cells. Nevertheless, SDR relies on high-precision parameter estimation for reconstruction, which tends to suffer from low signal-to-noise ratio (SNR) conditions and inevitably leads to artifacts that seriously affect the accuracy of SR reconstruction. In this Letter, a physics-enhanced neural network-based parameter-free SDR (PNNP-SDR) is proposed, which can achieve SR reconstruction directly in the spatial domain. As a result, the peak-SNR (PSNR) of PNNP-SDR is improved by about 4 dB compared to the cross-correlation (COR) SR reconstruction; meanwhile, the reconstruction speed of PNNP-SDR is even about five times faster than the fast approach based on principal component analysis (PCA). Given its capability of achieving parameter-free imaging, noise robustness, and high-fidelity and high-speed SR reconstruction over conventional SIM microscope hardware, the proposed PNNP-SDR is expected to be widely adopted in biomedical SR imaging scenarios.

Tailored antisense oligonucleotides designed to correct aberrant splicing reveal actionable groups of mutations for rare genetic disorders

Effective translation of rare disease diagnosis knowledge into therapeutic applications is achievable within a reasonable timeframe; where mutations are amenable to current antisense oligonucleotide technology. In our study, we identified five distinct types of abnormal splice-causing mutations in patients with rare genetic disorders and developed a tailored antisense oligonucleotide for each mutation type using phosphorodiamidate morpholino oligomers with or without octa-guanidine dendrimers and 2′-O-methoxyethyl phosphorothioate. We observed variations in treatment effects and efficiencies, influenced by both the chosen chemistry and the specific nature of the aberrant splicing patterns targeted for correction. Our study demonstrated the successful correction of all five different types of aberrant splicing. Our findings reveal that effective correction of aberrant splicing can depend on altering the chemical composition of oligonucleotides and suggest a fast, efficient, and feasible approach for developing personalized therapeutic interventions for genetic disorders within short time frames.

Pedotransfer functions development for modeling FC and PWP using Vis-NIR spectra combined with PLSR and regression models

The utilization of the soil pedotransfer functions (PTFs) developed based on the basic soil propertied is an alternative, fast, cost-effective and applicable approach for the prediction of field capacity (FC) and permanent wilting point (PWP). In addition, the Visible–Near-Infrared (Vis-NIR) spectra in soil science has gained prominence due to its practicality and relevance. In this paper, we used the data of the soil PWP and FC of 135 soil samples, easily measurable soil properties and Vis-NIR spectroscopy. The multiple linear regression (MLR) model was utilized to formulate PTFs model and Vis-NIR spectroscopy combined with MLR and partial least-squares (PLSR) was used to develop Spectrotransfer Function (STF). Results showed that among the easily measurable soil properties, particle-size diameter (dg) with Beta of −0.72 and −0.63 the most influential parameters for predicting FC and PWP, respectively, followed by the clay content. Developed PTFs for both FC and PWP with a R2 of 0.71 and 0.68, respectively, had a better performance than other previous developed PTFs. Results also revealed that the PLSR with a higher R2 (0.81) and lower RMSE (4 %) significantly performed better in comparison to STF for both FC and PWP prediction.

Surface Green's functions for an anisotropic viscoelastic half-plane and their application to contact problems

In this paper, the elastic-like surface Green's functions for an anisotropic viscoelastic half-plane are derived using the time-stepping method. Using the elastic-like surface Green's functions as the core analytical solutions, we develop semi-analytical models (SAMs) and apply them to solve two different contact problems with anisotropic viscoelastic materials. As new modeling approaches, the SAMs developed here can provide fast and efficient approaches to solving contact problems. These methods enable us to consider contact problems with generally anisotropic viscoelastic solids, in which the contact surface is frictional and either smooth or rough, and the applied loads and boundaries can be time-variant. The correctness of the derived surface Green's functions is demonstrated by comparing the numerical results obtained by SAMs and those achieved from the analytical solutions or boundary element methods. Using the obtained numerical results, the impacts of time step size, anisotropy, frictional coefficient, roughness, and applied loads on the contact responses are further analyzed and discussed.

Exploiting CO2 laser to boost graphite inks electron transfer for fructose biosensing in biological fluids

The possibility to print electronics by means of office tools has remarkedly increased the possibility to design affordable and robust point-of-care/need devices. However, conductive inks suffer from low electrochemical and rheological performances limiting their applicability in biosensors. Herein, a fast CO2 laser approach to activate printed carbon inks towards direct enzymatic bioelectrocatalysis (3rd generation) is proposed and exploited to build biosensors for D-fructose analysis in biological fluids.The CO2 laser treatment was compared with two lab-grade printed transducers fabricated with solvent (SB) and water (WB) based carbon inks. The use of the laser revealed significant morpho-chemical variations on the printed inks and was investigated towards enzymatic direct catalysis, using Fructose dehydrogenase (FDH) integrated into entirely lab-produced biosensors. The laser-driven activation of the inks unveils the inks' direct electron transfer (DET) ability between FDH and the electrode surface. Sub-micromolar limits of detection (SB-ink LOD = 0.47 μM; WB-ink LOD = 0.24 μM) and good linear ranges (SB-ink: 5–100 μM; WB-ink: 1–50 μM) were obtained, together with high selectivity due to use of the enzyme and the low applied overpotential (0.15 V vs. pseudo-Ag/AgCl). The laser-activated biosensors were successfully used for D-fructose determination in complex synthetic and real biological fluids (recoveries: 93–112%; RSD ≤8.0%, n = 3); in addition, the biosensor ability for continuous measurement (1.5h) was also demonstrated simulating physiological D-fructose fluctuations in cerebrospinal fluid.

Streaming prediction-error filters

Prediction-error filters (PEFs) are essential for seismic deconvolution and other geophysical estimation problems. We find that nonstationary multidimensional PEFs can be computed in a “streaming” manner, wherein the filter is updated incrementally by accepting one new data point at a time. The computational cost of estimating a streaming PEF is reduced to the cost of a single convolution. In other words, the cost of the PEF design while filtering is equivalent to the cost of applying the filter. Moreover, the nonlinear operation of finding and applying a streaming PEF is invertible at a similar cost, which enables a fast approach for missing data interpolation.

MuCoCP: a priori chemical knowledge-based multimodal contrastive learning pre-trained neural network for the prediction of cyclic peptide membrane penetration ability.

There has been a burgeoning interest in cyclic peptide therapeutics due to their various outstanding advantages and strong potential for drug formation. However, it is undoubtedly costly and inefficient to use traditional wet lab methods to clarify their biological activities. Using artificial intelligence instead is a more energy-efficient and faster approach. MuCoCP aims to build a complete pre-trained model for extracting potential features of cyclic peptides, which can be fine-tuned to accurately predict cyclic peptide bioactivity on various downstream tasks. To maximize its effectiveness, we use a novel data augmentation method based on a priori chemical knowledge and multiple unsupervised training objective functions to greatly improve the information-grabbing ability of the model. To assay the efficacy of the model, we conducted validation on the membrane-permeability of cyclic peptides which achieved an accuracy of 0.87 and R-squared of 0.503 on CycPeptMPDB using semi-supervised training and obtained an accuracy of 0.84 and R-squared of 0.384 using a model with frozen parameters on an external dataset. This result has achieved state-of-the-art, which substantiates the stability and generalization capability of MuCoCP. It means that MuCoCP can fully explore the high-dimensional information of cyclic peptides and make accurate predictions on downstream bioactivity tasks, which will serve as a guide for the future de novo design of cyclic peptide drugs and promote the development of cyclic peptide drugs. All code used in our proposed method can be found at https://github.com/lennonyu11234/MuCoCP.

Geoid Undulation Model as Vertical Reference in Indonesia

Indonesia released a new regional geoid model in 2020—the Indonesian Geoid 2020 (INAGEOID2020). It covers the Indonesian region with a spatial resolution of 0.01 × 0.01 degree with the unit in meters. The model was generated through a series of data and computations. Three components of gravity data, i.e., the observed free-air anomaly, the long-wave from the global geoid model, and the short-wave from the terrain model, were employed. The computation was performed using the Remove-Compute-Restore technique with the Fast Fourier Transformation approach. The output was then fitted to the geoid at tide stations by adding a fitting plane to the geoid model. The fitting plane was constructed based on the difference between the geoid model and each tide gauge benchmark. The final geoid model was evaluated by comparing the model with the reference data. Based on quality metrics, the accuracy of INAGEOID2020 varied between 6 cm to 29 cm. Any interested user can use this gridded geoid model to convert geodetic to orthometric heights and vice versa.

A Novel Method for the Assessment of Feeding Rate as a Phenotypic Endpoint for the Impact of Pollutants in Daphnids.

Traditional approaches for monitoring aquatic pollution primarily rely on chemical analysis and the detection of pollutants in the aqueous environments. However, these methods lack realism and mechanistic insight and, thus, are increasingly supported by effect-based methods, which offer sensitive endpoints. In this context, daphnids, a freshwater species used extensively in molecular ecotoxicology, offer fast and noninvasive approaches to assess the impact of pollutants. Among the phenotypic endpoints used, feeding rate is a highly sensitive approach because it provides evidence of physiological alterations even in sublethal concentrations. However, there has been no standardized method for measuring feeding rate in daphnids, and several approaches follow different protocols. There is a diversity among tests employing large volumes, extensive incubation times, and high animal densities, which in turn utilize measurements of algae via fluorescence, radiolabeling, or counting ingested cells. These tests are challenging and laborious and sometimes require cumbersome instrumentation. In the present study, we optimized the conditions of a miniaturized fast, sensitive, and high-throughput assay to assess the feeding rate based on the ingestion of fluorescent microparticles. The protocol was optimized in neonates in relation to the concentration of microplastic and the number of animals to increase reproducibility. Daphnids, following exposures to nonlethal concentrations, were incubated with microplastics; and, as filter feeders, they ingest microparticles. The new approach revealed differences in the physiology of daphnids in concentrations below the toxicity limits for a range of pollutants of different modes of action, thus proving feeding to be a more sensitive and noninvasive endpoint in pollution assessment. Environ Toxicol Chem 2024;43:2211-2221. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.