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Microfluidic free-flow electrophoresis (μFFE) is the most promising technique for proteomics. This method can perform real-time separation and detection of analytes in a small device ...where a continuous flow of carrier buffer is driven and an external electric field is applied perpendicular to the buffer flow. The capability of μFFE has motivated extensive applications pertaining to the pre-fractionation, enrichment, and higher-level purification of target proteins in biological systems. This review introduces the proteomics applications of the technique, along with a detailed theoretical overview, as follows. First, the principle and the band broadening involved in μFFE are explained. Next, materials for the fabrication of a μFFE device are described, followed by a summary of the online detection methods for μFFE. Finally, various applications of μFFE in proteomics fields are introduced, particularly focusing on microfluidic free-flow zone electrophoresis and microfluidic free-flow isoelectric focusing, the two major separation modes of μFFE.
Fluorine NMR spectroscopy is widely used for detection of protein–ligand interactions in drug discovery because of the simplicity of fluorine spectra combined with a relatively high likelihood for a ...drug molecule to include at least one fluorine atom. In general, an important limitation of NMR spectroscopy in drug discovery is its sensitivity, which results in the need for unphysiologically high protein concentrations and large ligand:protein ratios. An enhancement in the 19F signal of several thousand fold by dynamic nuclear polarization allows for the detection of submicromolar concentrations of fluorinated small molecules. Techniques for exploiting this gain in signal to detect ligands in the strong-, intermediate-, and weak-binding regimes are presented. Similar to conventional NMR analysis, dissociation constants are determined. However, the ability to use a low ligand concentration permits the detection of ligands in slow exchange that are not easily amenable to drug screening by traditional NMR methods. The relative speed and additional information gained may make the hyperpolarization-based approach an interesting alternative for use in drug discovery.
The typical filters that protect us from harmful components, such as toxic gases and particulate matter (PM), are made from petroleum-based materials, which need to be replaced with other ...environmentally friendly materials. Herein, we demonstrate a route to fabricate biodegradable and dual-functional filtration membranes that effectively remove PM and toxic gases. The membrane was integrated using two layers: (i) cellulose-based nanofibers for PM filtration and (ii) metal–organic framework (MOF)-coated cotton fabric for removal of toxic gases. Zeolitic imidazolate framework (ZIF-8) was grown from the surface of the cotton fabric by the treatment of cotton fabric with an organic precursor solution and subsequent immersion in an inorganic precursor solution. Cellulose acetate nanofibers (NFs) were deposited on the MOF-coated cotton fabric via electrospinning. At the optimal thickness of the NF layer, the quality factor of 18.8 × 10−2 Pa−1 was achieved with a filtration efficiency of 93.1%, air permeability of 19.0 cm3/cm2/s, and pressure drop of 14.2 Pa. The membrane exhibits outstanding gas adsorption efficiencies (>99%) for H2S, formaldehyde, and NH3. The resulting membrane was highly biodegradable, with a weight loss of 62.5% after 45 days under standard test conditions. The proposed strategy should provide highly sustainable material platforms for practical multifunctional membranes in personal protective equipment.
This study describes the development of a semi-physical, real-time nitric oxide (NO) prediction model that is capable of cycle-by-cycle prediction in a light-duty diesel engine. The model utilizes ...the measured in-cylinder pressure and information obtained from the engine control unit (ECU). From the inputs, the model takes into account the pilot injection burning and mixing, which affects the in-cylinder mixture formation. The representative in-cylinder temperature for NO formation was determined from the mixture composition calculation. The selected temperature and mixture composition was substituted using a simplified form of the NO formation rate equation for the cycle-by-cycle estimation. The reactive area and the duration of NO formation were assumed to be limited by the fuel quantity. The model predictability was verified not only using various steady-state conditions, including the variation of the EGR rate, the boost pressure, the rail pressure, and the injection timing, but also using transient conditions, which represent the worldwide harmonized light vehicles test procedure (WLTC). The WLTC NO prediction results produced less than 3% error with the measured value. In addition, the proposed model maintained its reliability in terms of hardware aging, the changing and artificial perturbations during steady-state and transient engine operations. The model has been shown to require low computational effort because of the cycle-by-cycle, engine-out NO emission prediction and control were performed simultaneously in an embedded system for the automotive application. We expect that the developed NO prediction model can be helpful in emission calibration during the engine design stage or in the real-time controlling of the exhaust NO emission for improving fuel consumption while satisfying NO emission legislation.
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This study introduces a facile method for synthesizing covalently bonded magnetic carbon nanoparticles (MCNs) in which carboxylic acid-functionalized activated carbon nanospheres ...(ACN-COOH) are connected with amine-terminated iron oxide nanoparticles (NPs) (Fe3O4-NH2) via a carbodiimide crosslinking reaction. The adsorption characteristics of the developed magnetic nanoparticles (ACN-Fe3O4) were investigated using a standard cationic dye (methylene blue, MB). Two additional MCNs (multi-core and core@shell structures) were also prepared, and their adsorption performances were extensively compared. The developed ACN-Fe3O4 material thoroughly utilizes the strengths of activated carbon and Fe3O4 themselves, exhibiting large specific surface areas (708.4 m2/g) and strong magnetic properties (40.3 emu/g), resulting in high adsorption capacity (349.5 mg/g) and recycling efficiency (76 % of adsorption performance after four cycles). In addition, a study of the mechanism reveals that pore-filling processes are dominant with minor contributions from electrostatic interactions, π–π interactions, and n–π interactions. The developed covalently bonded magnetic carbon nanoparticles (ACN-Fe3O4) can thus be considered as competent adsorbents with the potential to compensate for the drawbacks of contemporary MCNs, such as, low adsorption capacity, and weak magnetic properties.
In this study, we investigated potentially probiotic Bacillus licheniformis strains isolated from traditional Korean food sources for ability to enhance longevity using the nematode Caenorhabditis ...elegans as a simple in vivo animal model. We first investigated whether B. licheniformis strains were capable of modulating the lifespan of C. elegans. Among the tested strains, preconditioning with four B. licheniformis strains significantly enhanced the longevity of C. elegans. Unexpectedly, plate counting and transmission electron microscopy (TEM) results indicated that B. licheniformis strains were not more highly attached to the C. elegans intestine compared with Escherichia coli OP50 or Lactobacillus rhamnosus GG controls. In addition, qRT-PCR and an aging assay with mutant worms showed that the conditioning of B. licheniformis strain 141 directly influenced genes associated with serotonin signaling in nematodes, including tph-1 (tryptophan hydroxylase), bas-1 (serotonin- and dopamine-synthetic aromatic amino acid decarboxylase), mod-1 (serotonin-gated chloride channel), ser-1, and ser-7 (serotonin receptors) during C. elegans aging. Our findings suggest that B. licheniformis strain 141, which is isolated from traditional Korean foods, is a probiotic generally recognized as safe (GRAS) strain that enhances the lifespan of C. elegans via host serotonin signaling.
Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association ...between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly.
We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', r2 and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient (r2). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis.
SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. Diverse analysis methods, capability of handling huge data and visual comparison of analysis results are very comprehensive and easy-to-use.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Gold nanoparticles (AuNPs) have been used widely as multifunctional materials for several biomedical applications due to their attractive characteristics. However, toxicity and aggregation of AuNPs ...are critical issues, and methods of effective surface modification are required to overcome these problems. In this study, porous silicon‐coated gold nanoparticles (AuNP@pSi) were fabricated as a hybrid nanocomposite capable of surface‐enhanced Raman scattering (SERS)‐sensing and drug carrier. First, size‐controlled AuNPs were coated with a silica nano‐shell, and the resulting silica layers were converted to porous silicon through magnesiothermic reduction. Overall results suggest that AuNP@pSi can be exploited as a SERS probe with efficient Raman signal improvement of benzenethiol as well as a drug carrier based on its high surface area (113.7 m2 g−1) and porosity (13.38 nm, 0.3805 cm3 g−1). Since the porous silicon possibly can serve as magnetic resonance imaging probes with DNP technology, this hybrid platform potentially can be utilized as powerful material capable of theragnosis system.
In this study, we demonstrated a method of synthesizing porous silicon‐coated gold nanoparticles (AuNPs@pSi) with high control over particle size and morphology. AuNPs@pSi were fabricated as a hybrid nanocomposite capable of surface‐enhanced Raman scattering (SERS)‐sensing and drug carrier. The nanoparticles were characterized physically and chemically, and the overall configuration of the materials could be controlled selectively as either a core–shell structure or multicore structure by adjusting the reduction conditions. Brunauer Emmett Teller analysis revealed that AuNPs@pSi had a large surface area and a large number of pores; this lays the groundwork for further applications as a drug delivery system. The impacts of AuNP size on photothermal effect and SERS effect, as well as hyperpolarization features via dynamic nuclear polarization, are currently being investigated. We believe that this hybrid nanoplatform can be used to develop fascinating multifunctional applications that are capable of theragnosis.
The process used by engine manufacturers for the development of a new engine includes the planning and conceptual design phases, followed by the detailed design phase, in which the design target ...specifications are met. In the conceptual design phase, a rough specification of the target engine is presented to facilitate a detailed design and the additional cost of modification is reduced exponentially. In the conceptual design phase, however, not only is there no real engine. but there are also no 1D and 3D models present, so it is impossible to test and simulate them. Therefore, at this stage, a model that can predict emission and performance only according to the specifications and operating conditions of the engine would be very useful. Previous studies developed an EGR prediction model that can be used in the 0-D NOx prediction using a deep learning method. In this study, a NOx prediction model with high accuracy using only the operating conditions as input variables, without ECU data, was developed using deep neural networks. The developed model has high accuracy with an R-square of 0.988. The feature of this model is that all the input parameters for the deep neural network come from the operating conditions of the engine. Therefore, this model can be used in the early stages of the development of new engines when testing and simulation cannot be performed because they do not exist. The designer can set the range of the operating conditions such that they do not exceed the NOx limits at the specific operating point (specific rpm and BMEP). This variable operating design methodology is expected to be useful in the development of new engines for automobile manufacturers with various engine data.
Most of the parameters needed to predict Nitrogen Oxides (NOx) emissions, for example, combustion temperature, oxygen concentration and in-cylinder composition ratio, can be predicted by ...phenomenological 0-D prediction model if accurate EGR rates are provided. However, it is difficult to predict the EGR rate itself accurately by the model, so the EGR rate is predicted by the temperature measurement method. Although this method predicts EGR rates very accurately and quickly, there are some problems such as thermocouple failures and the difficulty in applying to mass production engines, so it is necessary to predict EGR rates by another method. The deep learning method follows an inductive methodology that extracts common characteristics of data based on a lot of data themselves. Therefore, although it requires a lot of experimental data, it has an advantage of high accuracy that can be obtained without any feature engineering. In this study, the EGR rate, which was difficult to predict in the past, was predicted by making various models using the deep learning method. Finally, EGR rate was predicted with a high accuracy of R-square 0.9994 and root mean squared error 0.0692 using a deep learning method at 1500 rpm and bmep 4, 6 and 8 bar. This study can be used as a basic study to predict EGR rates in transient and RDE conditions.
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