Experimental Parameters Research Articles

Multiplexing the Identification of Microorganisms via Tandem Mass Tag Labeling Augmented by Interference Removal through a Novel Modification of the Expectation Maximization Algorithm.

Having fast, accurate, and broad spectrum methods for the identification of microorganisms is of paramount importance to public health, research, and safety. Bottom-up mass spectrometer-based proteomics has emerged as an effective tool for the accurate identification of microorganisms from microbial isolates. However, one major hurdle that limits the deployment of this tool for routine clinical diagnosis, and other areas of research such as culturomics, is the instrument time required for the mass spectrometer to analyze a single sample, which can take ∼1 h per sample, when using mass spectrometers that are presently used in most institutes. To address this issue, in this study, we employed, for the first time, tandem mass tags (TMTs) in multiplex identifications of microorganisms from multiple TMT-labeled samples in one MS/MS experiment. A difficulty encountered when using TMT labeling is the presence of interference in the measured intensities of TMT reporter ions. To correct for interference, we employed in the proposed method a modified version of the expectation maximization (EM) algorithm that redistributes the signal from ion interference back to the correct TMT-labeled samples. We have evaluated the sensitivity and specificity of the proposed method using 94 MS/MS experiments (covering a broad range of protein concentration ratios across TMT-labeled channels and experimental parameters), containing a total of 1931 true positive TMT-labeled channels and 317 true negative TMT-labeled channels. The results of the evaluation show that the proposed method has an identification sensitivity of 93-97% and a specificity of 100% at the species level. Furthermore, as a proof of concept, using an in-house-generated data set composed of some of the most common urinary tract pathogens, we demonstrated that by using the proposed method the mass spectrometer time required per sample, using a 1 h LC-MS/MS run, can be reduced to 10 and 6 min when samples are labeled with TMT-6 and TMT-10, respectively. The proposed method can also be used along with Orbitrap mass spectrometers that have faster MS/MS acquisition rates, like the recently released Orbitrap Astral mass spectrometer, to further reduce the mass spectrometer time required per sample.

Residual Stress Evaluation in L-PBF-Produced SS 316L Specimens

The identification of residual stresses (RS) in components made by selective laser melting (SLM) is necessary for subsequent technological optimization. The presented research is devoted to evaluating the influence of the combination of laser power (P), scanning velocity (v) and the rarely considered number of layers (nL) on surface residual stresses in SLM stainless steel SS 316L. Experimental parameters were set based on the Design of Experiment (DoE) method, with follow-up X-ray diffraction (XRD) measurements and data processing using analysis of variance (ANOVA) and regression analysis. The obtained data are a valuable stepping-stone for the subsequent design of research focused on the application of sustainable eco-friendly Abrasive Water Jet (AWJ) peening for RS modification in the evaluated material.

Development of a chitosan-gold nanocomposite based disposable pencil graphite electrode for the selective detection of thiram in peach juice

ABSTRACT Hazardous chemicals that are mostly employed in modern agriculture for pest management have caused long-term environmental accumulation as well as major issues with food safety and human health. So, there is an urgent demand for efficient detection methods for environmental monitoring. To do this, the most contemporary electrochemical sensor technology is suggested. In this study, a new electrochemical sensor has been developed to detect thiram (Th) fungicide, utilising a modified activated pencil graphite electrode (a-PGE) made from chitosan-gold nanoparticle polymer (Ch-AuNPs) for the first time. The Ch-AuNPs/a-PGE surface has been characterised using cyclic voltammetry (CV), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDS). Following adjustments to experimental parameters, the proposed electrochemical sensor demonstrated a linear calibration curve within the range of 4.90–140 μM, displaying a rapid response to thiram with a detection limit of 1.15 μM. The sensor exhibited notable selectivity, stability, and reproducibility. To assess its practical utility, the sensor was applied to analyse thiram in peach juice, and the recovery process indicated its effectiveness as a practical, swift, and efficient tool in pesticide residue monitoring.

HiPSC-CM Electrophysiology: Impact of Temporal Changes and Study Parameters on Experimental Reproducibility.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are frequently used for preclinical cardiotoxicity testing and remain an important tool for confirming model-based predictions of drug effects in accordance with Comprehensive in Vitro Proarrhythmia Assay (CiPA). Despite the considerable benefits hiPSC-CMs provide, concerns surrounding experimental reproducibility have emerged. We investigated the effects of temporal changes and experimental parameters on hiPSC-CM electrophysiology. iCell cardiomyocytes2 were cultured and biosignals were acquired using a microelectrode array (MEA) system (1-14 days). Continuous recordings revealed a 22.6% increase in the beating rate and 7.7% decrease in the field potential duration (FPD) during a 20-minute equilibration period. Location specific differences across a multiwell plate were also observed, with iCell cardiomyocytes2 in the outer rows beating 8.8 beats per minute (BPM) faster than the inner rows. Cardiac endpoints were also impacted by cell culture duration; from 2-14 days the beating rate decreased (-12.7 BPM), FPD lengthened (+257 ms), and spike amplitude increased (+3.3 mV). Cell culture duration (4-10 days) also impacted cardiomyocyte drug responsiveness (E-4031, nifedipine, isoproterenol). qRT-PCR results suggest that daily variations in cardiac metrics may be linked to the continued maturation of hiPSC-CMs in culture (2-30 days). Daily experiments were also repeated using a second cell line (Cor.4U). Collectively, our study highlights multiple sources of variability to consider and address when performing hiPSC-CM MEA studies. To improve reproducibility and data interpretation, MEA-based studies should establish a standardized protocol and report key experimental conditions (e.g., cell line, culture time, equilibration time, electrical stimulation settings, raw data values).

Optimal Summary Statistics for X-ray Polarization

We develop two new highly efficient estimators to measure the polarization (Stokes parameters) in experiments that constrain the position angle of individual photons such as scattering and gas-pixel-detector polarimeters, and analyse in detail a previously proposed estimator. All three of these estimators are at least fifty percent more efficient on typical datasets than the standard estimator used in the field. We present analytic estimates of the variance of these estimators and numerical experiments to verify these estimates. Two of the three estimators can be calculated quickly and directly through summations over the measurements of individual photons.

Electrochemical Immunoassay Platform for Human Monkeypox Virus Detection.

In this study, we reported a selective impedimetric biosensor for the detection of A29 which is the target protein of the monkeypox virus (MPXV). The working principle of the biosensor relies on the interaction mechanism between A29, which is an internal membrane protein of MPXV, and the heparan sulfate receptor. For this purpose, after immobilizing heparan sulfate onto the gold screen-printed electrode surface, its interaction with A29 protein was monitored using electrochemical impedance spectroscopy. After the optimization of experimental parameters, the analytical characteristics of the developed MPVX immunosensor were examined. The developed immunosensor exhibited a linear detection range between 2.0 and 50 ng mL-1, with a detection limit of 2.08 ng mL-1 and a quantification limit of 6.28 ng mL-1. Furthermore, a relative standard deviation value of 2.82% was determined for 25 ng mL-1. Apart from that, sample application studies were also performed with the standard addition of A29 protein to 1:10 diluted real serum samples that were taken from healthy individuals, and very good recovery values were obtained.

State-dependent misalignment and turbulence effects on high-dimensional quantum key distribution with orbital angular momentum

Abstract High-dimensional quantum key distribution (HD-QKD) is a topic of growing interest in the quantum communication community, not only for its inherent properties but also for its possible applications. As the typical freedom in HD-QKD, orbital angular momentum (OAM) has made significant advancements in experiments recently. However, in the airborne scenario, different states suffer different amounts of misalignment and turbulence. A complete theoretical analysis model for the transmission characteristics of OAM in atmospheric channels is lacking. In this paper, we systematically analyze the extent to which degeneration including channel power loss and mode crosstalk are influenced by misalignment and turbulence effects. Furthermore, the performance of OAM-encoded HD-QKD system in different dimensions is evaluated while incorporating finite-key effects. We demonstrate that the performance of OAM-encoded HD-QKD will be better at short range, which provide a reference to implement QKD based on task requirements. Since OAM is desired to increase the capacity of QKD system and experiments have already been carried out, our work can not only bridge the gap between theory and practice, but also optimize experimental parameters and improve system performance.

Phase-contrast neutron imaging compared with wave propagation and McStas simulations

Propagation-based phase contrast, for example in the form of edge enhancement contrast, is well established within X-ray imaging but is not widely used in neutron imaging. This technique can help increase the contrast of low-attenuation samples but may confuse quantitative absorption measurements. Therefore, it is important to understand the experimental parameters that cause and amplify or dampen this effect in order to optimize future experiments properly. Two simulation approaches have been investigated, a wave-based simulation and a particle-based simulation conducted in McStas [Willendrup & Lefmann (2020). J. Neutron Res. 22, 1–16], and they are compared with experimental data. The experiment was done on a sample of metal foils with weakly and strongly neutron absorbing layers, which were measured while varying the rotation angle and propagation distance from the sample. The experimental data show multiple signals: attenuation, phase contrast and reflection. The wave model reproduces the sample attenuation and the phase peaks but it does not reproduce the behavior of these peaks as a function of rotation angle. The McStas simulation agrees better with the experimental data, as it reproduces attenuation, phase peaks and reflection, as well as the change in these signals as a function of rotation angle and distance. This suggests that the McStas simulation approach, where the particle description of the neutron facilitates the incorporation of multiple effects, is the most convenient way of modeling edge enhancement in neutron imaging.

The effect of social anxiety on threat acquisition and extinction: a systematic review and meta-analysis.

Although exposure-based therapy has been found to be effective at alleviating symptoms of social anxiety disorder, it often does not lead to full remission, and relapse after treatment is common. Exposure therapy is based on theoretical principles of extinction of conditioned fear responses. However, there are inconsistencies in findings across experiments that have investigated the effect of social anxiety on threat conditioning and extinction processes. This systematic review and meta-analysis aimed to examine whether elevated levels of social anxiety are associated with abnormalities in threat conditioning and extinction processes. A second aim was to examine the sensitivity of various study designs and characteristics to detect social anxiety-related differences in threat conditioning and extinction. A systematic search was conducted, which identified twenty-three experiments for inclusion in the review. The findings did not demonstrate compelling evidence that high levels of social anxiety are associated with atypical threat conditioning or extinction. Further, when systematically examining the data, there was no convincing support that the use of a particular psychophysiological measure, subjective rating, or experimental parameter yields more consistent associations between social anxiety and conditioning processes during threat acquisition or extinction. Meta-analyses demonstrated that during threat extinction, the use of anxiety ratings as a dependent variable, socially relevant unconditioned stimuli, and a higher reinforcement schedule produced more detectable effects of social anxiety on compromised extinction processes compared to any other dependent variable (subjective or physiological) or experimental parameter. Overall, the results of this study suggest that social anxiety is not reliably related to deficits in conditioning and extinction processes in the context of laboratory-based Pavlovian conditioning paradigms.

Uniaxial stress effect on the electronic structure of quantum materials

Uniaxial stress has proven to be a powerful experimental tuning parameter for effectively controlling lattice, charge, orbital, and spin degrees of freedom in quantum materials. In addition, its ability to manipulate the symmetry of materials has garnered significant attention. Recent technical progress to combine uniaxial stress cells with quantum oscillation and angle-resolved photoemission techniques allowed to study the electronic structure as function of uniaxial stress. This review provides an overview on experimental advancements in methods and examines studies on diverse quantum materials, encompassing the semimetal WTe2, the unconventional superconductor Sr2RuO4, Fe-based superconductors, and topological materials.

Investigations of optical and thermal properties of surface layers of Cd1−xBexTe samples by the photoacoustic method

The work presents the results of a research on the photoacoustic spectra of thin surface layers of Cd1−xBexTe crystals formed by grinding and polishing their surfaces. As a result of matching the theoretical and experimental photoacoustic spectra, thermal and optical parameters of these layers were determined. Thermal parameters of the surface layers, such as thermal conductivity and thermal diffusivity, turned out to be much worse than the analogous parameters of the substrate. The increase in the optical absorption of surface layers for photon energies below Eg was also determined. Eg was also determined.

A physical simulation-machine learning model for optimal process schemes in laser-based directed energy deposition process

The major challenge faced is the definition of optimal process variables for rich quality of fabricated parts in laser-based directed energy deposition (DED-LB) processes. However, predicting the optimal process scheme using machine learning models is still challenging owing to the need for a large amount of training experimental data with high costs in DED-LB. In view of this, a physical simulation (PS)-machine learning (ML) model using a relatively small accurate data set of 31 simulation data for optimal process schemes is proposed in DED-LB process in this study. Firstly, a powder-scale high precision phenomenon model incorporating the mass transfer, phase transformations and heat transfer and using Lagrangian particle model to add mass is developed to depict the DED-LB process. Then, a Gaussian process regression (GPR) agent model is established to rapidly and accurately predict the geometry and dilution rate of deposition tracks under different manufacturing parameters based on the high precision simulation results. Finally, a PS-ML model for process parameter optimization is developed using the particle swarm algorithm (PSO), and the optimized parameters are experimentally validated. The results show that the developed powder-scale model and GPR model results are consistent with the Inconel 718 alloy experimental results. The proposed PS-ML model can increase the accuracy of the ML models even with a small simulation data set. None of PS-ML model optimization results has a relative error of more than 3% to the experimental results, and the dilution rate is reduced by up to 61.66% compared to the experimental design parameters without optimization. The proposed physical simulation-machine learning model in this study enables inexpensive and accurate optimization of DED-LB process parameters.

Cyclic behavior of laminated bio-based connections with slotted-in steel plates: Genetic algorithm, deterministic neural network-based model parameter identification, and uncertainty quantification

To support more sustainable construction, this paper experimentally investigates the cyclic behavior of laminated timber (Laminated Veneer Lumber (LVL)) and glubam (Glue Laminated Bamboo) connections with slotted-in steel plates in terms of experimental test, numerical simulations and parameter identification. Experimental tests included eight different configurations: two materials (LVL and glubam), two bolt diameters (8 and 10 mm), and one or two bolts. Two different cyclic-loading protocols were applied for each type of connection: only tension and tension/compression. The observed behavior is then compared to a finite element model developed in OpenSeesPy, which takes into account factors such as sliding, contact, pinching, cyclic stiffness, and strength degradation. To identify the best set of parameters for the model, three different approaches are considered: genetic algorithm, fast deterministic neural network, and probabilistic Bayesian method. First, the model identification is carried out by means of a genetic algorithm-based optimization. The parameter-identification results are evaluated in terms of elastic stiffness, yielding point, and ductility. Next, a sensitivity analysis is performed to determine the significance of the parameters, and an innovative approach combining neural network and sensitivity analysis is proposed for fast and preliminary parameter identification. Then, probabilistic Bayesian identification is employed to calculate the posterior distribution of the model parameters identified and the confidence bounds of the estimated response. Finally, different model identification parameters are compared and suggestions for algorithm selection are provided.

Experimental study on hysteretic behavior of energy dissipation braces with bending plates and perforated shear plates

The shear plates had been adopted in the energy dissipation braces, but the ductility of energy dissipation braces with shear plates was poor. In this paper, a new-type bending plate-strip coupling energy dissipation brace (BSEB) with bending plates and perforated shear plates was proposed, and the strips included non-uniform strips, stiffening non-uniform strips, and T-shaped strips. The hysteretic behavior of five BSEBs was investigated through a quasi-static test. The experimental parameters included boundary condition, arrangement of perforated shear plates, and material. The experimental results indicated that the initial stiffness of BSEBs with pinned connection was significantly lower than that with bolted connection; The influence of arrangement of perforated shear plates on seismic performance of BSEBs could be little; The BSEB with perforated shear plates fabricated by stainless steel had higher initial stiffness and ultimate bearing capacity, and lower energy dissipation capacity and ductility; The fracture occurred in stiffening non-uniform strips, T-shaped strips, non-uniform strips, and bending plates in turn, and plastic strain occurred in stiffening non-uniform strips, non-uniform strips, T-shaped strips, and bending plates in turn. Finally, a few formulas for predicting the yield bearing capacity of a BSEB were developed and verified.

Metabolomics analysis using matrix-assisted diffusion-ordered spectroscopy (DOSY) and its application in acrylamide-induced cardiovascular toxicity

Untargeted NMR-based metabolomics strategies are increasingly applied for metabolite screening in a wide variety of medical conditions. However, the identification of metabolites remains a great challenge in NMR metabolomics research. Here, we used matrix-assisted diffusion-ordered spectroscopy (DOSY) to identify new biomarkers in body fluids. DOSY-based metabolomics can exploit the unique strengths of NMR and, thanks to improvements in its resolution, can be successfully used to address a significantly wider range of biological issues. In this study, a metabolite model consisting of ten common metabolites was employed to optimize the matrix-assisted DOSY experimental parameters. Polyvinylpyrrolidone (PVP), a high-molecular-weight polymer, was found to be the most appropriate matrix with which to accomplish the separation of the mixture components. 1H NMR and PVP-assisted DOSY analyses were performed to characterize the metabolic profile of serum samples from acrylamide (ACR)-treated rats and study the mechanism of ACR-induced cardiovascular toxicity. Biomarker identification by 1H NMR and PVP-assisted DOSY yielded similar results. However, multivariate statistical analysis showed that, compared with 1H NMR, PVP-assisted DOSY achieved a better discrimination of the serum samples obtained from rats treated with different doses of ACR. A total of 11 differential metabolites were identified. An enrichment analysis of the metabolic pathways showed that, in rats treated with a high dose of ACR, the glutamate and glutathione metabolic pathways and the energy metabolism pathway of the tricarboxylic acid cycle were mainly affected.

Destruction of n-hexane from the air stream by pulsed discharge plasma: Modelling and key process parameters optimization by CCD-RSM

In this study, a Dielectric-Barrier Discharge (DBD) plasma reactor was used for the degradation of n-hexane from an air stream. a Central Composite Design (CCD) based Response Surface Methodology (RSM), (CCD-RSM) was utilized to explore the effects of experimental parameters including discharge voltage, initial concentration, gas flow rate, relative humidity and their interactions on the reactor performance. The proposed optimization model showed a satisfactory correlation between the predicted and actual results. According to the results, discharge voltage was the most significant parameter affecting the removal efficiency, CO2 selectivity and the level of O3 and NOx formation, whereas energy yield was mainly influenced by gas flow rate and initial concentration. At optimized conditions (discharge voltage of 8.5 kV, inlet concentration of 94.3 ppm, relative humidity of 55 % and gas flow rate of 733.16 ml/min with combined desirability of 0.921), removal efficiency of 92.57 %, CO2 selectivity of 58.87 %, energy efficiency of 0.25 g/kWh, O3 formation of 140.88 mg/m3 and NOx formation of 122.46 ppm were obtained. Water vapour significantly increased the removal efficiency and CO2 selectivity as well as reduced O3 and NOx. The results revealed that the CCD-RSM provided the optimum process parameters for the Non-Thermal Plasma (NTP) system.

Heavy Metal Ion Adsorbent in Aqueous Solution: A Review on Chitosan and Chitosan Composites

One prominent contributor to environmental degradation is the presence of heavy metal ions, which are commonly introduced into ecosystems through diverse channels, including mining operations, paint manufacturing, battery production, and agricultural practises. The unprocessed waste from these industries typically undergoes direct discharge into rivers and seas, thereby adversely influencing the potable water supply and marine ecosystem. This results in the accumulation of heavy metals within living organisms and subsequent environmental degradation that poses a significant risk to human health, as their prolonged consumption can harm various organs and the nervous system. Therefore, the United States Environmental Protection Agency (US-EPA) and the World Health Organisation (WHO) have established stringent regulations regarding the allowable levels of heavy metal intake, particularly concerning potable water consumption. The imperative lies in surmounting this challenge by discovering the optimal technique for extracting heavy metals. This paper examines the primary methods of eliminating heavy metal ions using chitosan-based materials. Specifically, the methods under scrutiny include electrocoagulation (E.C.), bioremediation, ion-exchange, membrane filtration (purification), and adsorption. Adsorption, as a highly efficient method, presents itself as a viable strategy for the remediation of dyes in textile wastewater due to its advantageous characteristics. Notably, this approach obviates the need for extensive spatial requirements and exhibits a commendably swift treatment duration. Chitosan exhibits promising adsorptive characteristics owing to its advantageous physical attributes, including crystalline structure, porous nature, and particle dimensions, all of which synergistically enhance the adsorption capacity of chitosan. Research findings indicate that the adsorption characteristics of chitosan are subject to variation contingent upon the distinct heavy metal ions under consideration and the specific experimental parameters employed. Hence, optimising these parameters becomes imperative to attain enhanced chitosan adsorption capabilities for heavy metal ion removal.

Studies on the Authorship of Albumen Vintage Photographs: A Combined Experimental and Chemometric Approach

The differences in albumen photographs from vintage photographic studios were identified by energy-dispersive X-ray fluorescence spectroscopy and Fourier transform infrared spectroscopy. The results inspired the concept of finding common features characteristic of a given photographic studio. The obtained measurement data (i.e., positions of vibrational bands for characteristic groups of albumen and the mass contents of chosen elements) were analyzed chemometrically by employing the Principal Component Analysis (PCA). The PCA technique allowed us to reduce the number of relevant experimental parameters characterizing the unique features of the photographic objects. The two major components were able to distinguish the photographic objects in terms of their authorship and the time to produce a photograph. The method developed was examined for a selected group of photographs consisting of albumen prints from three Polish photographic ateliers. To validate ED-XRF measurements and, consequently, the chemometric findings, reference albumen photo samples were designed and prepared. The empirical functional relationships between the content of photochemically reduced silver particles on the photographic paper and several physicochemical factors, including time of exposure to UV light, AgNO3 concentration in a fixed bath, and concentrations of other additives, were proposed. These results can be used for the prediction of the experimental conditions under which the investigated photographs were developed.

Fast and efficient extraction of phenol from aqueous phase using deep eutectic solvents: Experimental and density functional theory investigation for interactions studies

Phenol is carcinogenic and toxic to human beings and therefore it is necessary to remove this toxic pollutant from water. Hydrophobic deep eutectic solvents (DESs) were prepared using trioctylamine (TOA) as hydrogen bond acceptor (HBA) and 2-hydroxybenzoic acid and 4-dodecylbenzenesulfonic acid as hydrogen bond donors (HBDs) for extraction of phenol from aqueous media. Fourier transformer infrared (FTIR) spectroscopy and thermogravimetric analysis (TGA) were performed for functional group and thermal stability determination, respectively. Phenol extraction efficiency of DESs was studied under different experimental parameters, such as initial pH of phenol solution (2–10), contact time (1–15 min), mixing ratio of volume of DESs to aqueous media (VDES:Vaq.) (1:10–1:50 mL), initial concentration of phenol (1000–5000 ppm) and temperature (25 °C–55 °C). More than 97 % extraction efficiency of DES for phenol was achieved using contact time of 5 min, initial phenol concentration of 2500 ppm, VDES:Vaq in a ratio of 1:10 mL, and temperature of 25 °C. The values of ΔG° calculated for phenol extraction at various temperatures have negative values, confirming that the phenol movement from water phase to DESs phase is spontaneous in nature. Density functional theory (DFT) simulations were conducted to study the interaction of DESs with water and phenol. In addition, DFT was used to determine the interaction energy of DESs with water and phenol. The considerably higher interaction energies (−20.4 and 18.2 kcal/mol) obtained with both DESs for phenol over water emphasize the selective separation efficiency of DESs for phenol. The experimental results confirmed that these synthesised DESs can effectively extract phenol from the aqueous phase to the organic phase.

Nano-revolution in heavy metal removal: engineered nanomaterials for cleaner water

Nano-revolution in heavy metal removal: engineered nanomaterials for cleaner water