Analytical science advances最新文献

Cell-free biosynthesis is emerging as a very attractive alternative for the production of market-relevant molecules. The free combination of enzymes, regardless of where they are isolated from, raises the possibility to build more efficient synthetic routes but at the same time leads to higher complexity regarding the analysis of the different enzymatic steps. Here we present an analytical method for the real-time analysis of acyl-CoA blocks forming and consuming during multi-step catalyses. We focused on malonyl-Coenzyme A and acetyl-CoA, which are the most used acyl-CoA units for carbon chain elongations. By employing capillary electrophoresis, we could detect the decrease of educts and the formation of products in a time-resolved fashion.

We measured Raman spectra of N₂ fluids obtained at 0.1–25 MPa at room temperature. The ¹⁴N¹⁵N peak in the Raman spectrum of a low-pressure N₂ fluid is difficult to detect because of the prevalence of a group of peaks attributed to rotational vibration of ¹⁴N₂. The Raman peaks of ¹⁴N¹⁵N and ¹⁴N₂ of N₂ fluid at 25 MPa were acquired at various exposure times. The mean values and standard deviations of the peak height ratios and peak area ones of ¹⁴N¹⁵N and ¹⁴N₂ were examined for each time. The standard deviations of the peak height ratios and peak area ones were 2.2% and 1.9%, respectively, for 20 spectra acquired with peak height of 1 million counts of ¹⁴N₂. The uncertainties are about two times higher than those predicted from the noise of a CCD. Improvement of the pixel resolution can enhance the precision of the nitrogen isotope ratios by Raman spectroscopy.

Inorganic arsenic is a carcinogen repeatedly found in water and foods threatening global human health. Prior work applied the Gutzeit method and X-ray fluorescence spectroscopy to quantify inorganic arsenic based on a harmful chemical, i.e., mercury bromide, to capture the arsine gas. In this project, we explored silver nitrate as an alternative to mercury bromide for the capture and detection of inorganic arsenic. To compare the performance of mercury bromide and silver nitrate, two standard curves were established in the range from 0 to 33.3 µg/L after optimization of reaction conditions such as the quantity of reagents and reaction time. Our result shows silver nitrate-based standard curve had a lower limit of detection and limit of quantification at 1.02 µg/L and 3.40 µg/L, respectively, as compared to the one built upon mercury bromide that has limit of detection of 4.86 µg/L and limit of quantification of 16.2 µg/L. The relative higher sensitivity when using silver nitrate was contributed by the less interfering elements for X-ray fluorescence analysis and thus lower background signals. A commercial apple juice was studied for matrix inference, and the results show 85%–99% recoveries and 7.4%–24.5% relative standard deviation. In conclusion, we demonstrated silver nitrate is a better choice in terms of safety restrictions and detection capability at lower inorganic arsenic concentrations.

In molecular design, material design, process design, and process control, it is important not only to construct a model with high predictive ability between explanatory features x and objective features y using a dataset but also to interpret the constructed model. An index of feature importance in x is permutation feature importance (PFI), which can be combined with any regressors and classifiers. However, the PFI becomes unstable when the number of samples is low because it is necessary to divide a dataset into training and validation data when calculating it. Additionally, when there are strongly correlated features in x, the PFI of these features is estimated to be low. Hence, a cross-validated PFI (CVPFI) method is proposed. CVPFI can be calculated stably, even with a small number of samples, because model construction and feature evaluation are repeated based on cross-validation. Furthermore, by considering the absolute correlation coefficients between the features, the feature importance can be evaluated appropriately even when there are strongly correlated features in x. Case studies using numerical simulation data and actual compound data showed that the feature importance can be evaluated appropriately using CVPFI compared to PFI. This is possible when the number of samples is low, when linear and nonlinear relationships are mixed between x and y when there are strong correlations between features in x, and when quantised and biased features exist in x. Python codes for CVPFI are available at https://github.com/hkaneko1985/dcekit.

Neospora caninum is an obligate intracellular protozoan parasite of the phylum Alveolata (subphylum Apicomplexa) which has not been studied extensively in a biochemical context. N. caninum is a primary cause of reproductive disorders causing mummification and abortion not only in cattle but also in other small ruminant species resulting in a substantial economic impact on the livestock industry. In canids, which are the final hosts of N. caninum, clinical disease includes neuromuscular symptoms, ataxia, and ascending paralysis. Fatal outcomes of neosporosis have also been reported depending on the host species, age and immune status, however, its zoonotic potential is still uncertain. Therefore, N. caninum should be thoroughly investigated. Matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry (MS) and MS imaging (MSI) were used, combined with high-performance liquid chromatography (HPLC) to investigate these intracellular parasites. The aim of this study was to identify molecular biomarkers for N. caninum tachyzoite-infected host cells and to further clarify their functions. By atmospheric-pressure scanning microprobe MALDI MS(I), sections of N. caninum-infected and non-infected host cell pellets were examined in order to determine potential markers. In vivo, N. caninum infects different types of nucleated cells, such as endothelial cells which represent a highly immunoreactive cell type. Therefore, primary bovine umbilical vein endothelial cells were here used as a suitable infection system. For comparison, the permanent MARC-145 cell line was used as an additional, simplified in vitro cell culture model. HPLC-tandem MS (HPLC-MS/MS) experiments combined with database search were employed for structural verification of markers. The statistically relevant biomarkers found by MS and identified by HPLC-MS/MS measurements were partly also found in infected monolayers. Marker signals were imaged in cell layers of N. caninum-infected and non-infected host cells at 5 µm lateral resolution.

Bioactive peptides with a molecular mass between 2 and 10 kDa represent an important class of substances banned in elite sports, which has been recognized with an increasing number and variety of substances by anti-doping organizations. Also, the annually renewed list of prohibited substances of the World Anti-Doping Agency (WADA) explicitly mentions more and more of these peptides, and efficient testing procedures are required. Even under simplified sample preparation conditions, liquid chromatography coupled to high-resolution mass spectrometry (with resolution properties > 100,000 full width at half maximum) offers suitable conditions for this task and can therefore be used as an initial testing procedure. In contrast to urine, blood analysis essentially relies on the detection of intact peptide hormones, and the expected concentrations are commonly higher in blood samples than in urine. This facilitates the analysis, and a generic sample preparation by means of mixed-mode solid-phase extraction could be realized in this study. Co-extraction and analysis of several different peptides such as insulins (human, lispro, aspart, glulisine, tresiba, detemir, glargine, bovine insulin and porcine insulin), growth hormone releasing hormones (sermorelin, CJC-1295 and tesamorelin), insulin-like growth factors (long-R₃-IGF-I, R₃-IGF-I and Des_1-3-IGF-I) and mechano growth factors (human MGF and MGF-Goldspink) with criteria that fulfil the requirements of the WADA documents (TD2022 MRPL) for doping controls. The proof of principle was shown by the analysis of post administration samples after treatment with synthetic insulin analogues.

The copper slags collected during the excavation of the Early Harappan period site at Kunal in northern India were studied to understand the advancement of smelting technology and the achieved smelting temperature in the furnace by undertaking archaeometallurgical characterisation of the slags. In this research, two types of slags such as slag with glassy appearance and granulated slag were selected for the study. The microscopic structure and distribution of slag components were investigated using optical microscopy and phase determination was done by X-ray diffraction (XRD). Chemical characterization of the slags was conducted using scanning electron microscopy-energy-dispersive X-ray spectroscopy, X-ray fluorescence analysis (XRF) and inductively coupled plasma-mass spectrometry to build a complete chemical profile of the slags. Fayalite and magnetite were the dominant phases in the glassy slag; however, the granulated slag showed the dominance of calcite as secondary phase which reflected the dolomitic stoichiometry of the slag. The presence of dominant fayalite mineral phase as detected by XRD and higher concentration of iron as detected by XRF indicated the reducing environment during the smelting process. In this study, the absence of sulphur is reported which is unique to the ancient copper slag.

The cover image is based on the Research Article Resonance Raman analysis of intracellular vitamin B₁₂ analogs in methanogenic archaea by Nanako Kanno et al., https://doi.org/10.1002/ansa.202100042.

Analytic methods development, like many other disciplines, relies on experimentation and data analysis. Determining the contribution of a paper or report on a study incorporating data analysis is typically left to the reviewer's experience and good sense, without reliance on structured guidelines. This is amplified by the growing role of machine learning driven analysis, where results are based on computer intensive algorithm applications. The evaluation of a predictive model where cross validation was used to fit its parameters adds challenges to the evaluation of regression models, where the estimates can be easily reproduced. This lack of structure to support reviews increases uncertainty and variability in reviews. In this paper, aspects of statistical assessment are considered. We provide checklists for reviewers of applied statistics work with a focus on analytic method development. The checklist covers six aspects relevant to a review of statistical analysis, namely: (1) study design, (2) algorithmic and inferential methods in frequentism analysis, (3) Bayesian methods in Bayesian analysis (if relevant), (4) selective inference aspects, (5) severe testing properties and (6) presentation of findings. We provide a brief overview of these elements providing references for a more elaborate treatment. The robustness analysis of an analytical method is used to illustrate how an improvement can be achieved in response to questions in the checklist. The paper is aimed at both engineers and seasoned researchers.