Quantities Of Samples Research Articles

Differential DNA Extraction from Lateral Flow Immunochromatographic Tests via the EZ1® Advanced XL System

This differential extraction protocol details the steps for isolating DNA from sample pads used in lateral flow immunochromatographic (LFI) tests, particularly for cases involving mixed biological samples such as semen and menstrual blood, or other evidence related to sexual assault. This procedure utilizes a differential extraction technique applied to sample pads from immunochromatographic tests, where the sample pads serve as the substrate. The method involves two sequential lysis steps to effectively separate non-sperm and sperm fractions, enabling the targeted isolation of distinct cell types for downstream DNA analysis. The efficiency of this procedure is demonstrated by the results within this paper, which highlights the successful recovery of both male autosomal and Y-STR profiles, even in mixed samples with a high female presence. Overall, this protocol demonstrates the effective recovery of DNA from sample pads, which is beneficial for forensic practitioners dealing with limited sample quantities, underscoring the value of using these pads in forensic analysis.

Process parameters settings and quality investment selection for modified traditional inventory model

In this article, we propose the process parameters settings in the first phase. Then the extended problem with production quantity, quality investment, and parameters of acceptance sampling in the second phase. Taguchi’s loss cost is used for evaluating quality.

Advancements in Magnetic Nanoparticle Design: SiO2@Fe3O4 Core/Shell Nanoparticles with Size-Tunable Magnetic Responses

Thin magnetic films conformed to closed surfaces are theoretically predicted to exhibit exotic magnetic ground states at microscopic curvatures. However, there is a general lack of experimental reports on the subject, mostly associated to challenges in achieving the desired geometry at the nanoscale. In this work, we tackle this issue by experimentally probing magnetite nanoshells grown on the surface of bare silica nanospheres of 20 nm – 600 nm in diameter. Such a system can be described as magnetic centers arranged in a closed, non-flat film. Results reveal that the absence of a magnetic core results in shells with a curvature-dependent magnetic response which is markedly different to that of conventional bulk magnetic nanoparticles. We report that a granular aspect in such magnetic nanoshells negatively affects the formation of clear vortex signatures in measurements of macroscopic sample quantities. Micromagnetic simulations suggest this to be the result of the pinning of vortices on different regions of the sphere surface, leading to sharp, field-driven variations of the magnetic response of individual particles.

Application and Analysis of the MFF-YOLOv7 Model in Underwater Sonar Image Target Detection

The need for precise identification of underwater sonar image targets is growing in areas such as marine resource exploitation, subsea construction, and ocean ecosystem surveillance. Nevertheless, conventional image recognition algorithms encounter several obstacles, including intricate underwater settings, poor-quality sonar image data, and limited sample quantities, which hinder accurate identification. This study seeks to improve underwater sonar image target recognition capabilities by employing deep learning techniques and developing the Multi-Gradient Feature Fusion YOLOv7 model (MFF-YOLOv7) to address these challenges. This model incorporates the Multi-Scale Information Fusion Module (MIFM) as a replacement for YOLOv7’s SPPCSPC, substitutes the Conv of CBS following ELAN with RFAConv, and integrates the SCSA mechanism at three junctions where the backbone links to the head, enhancing target recognition accuracy. Trials were conducted using datasets like URPC, SCTD, and UATD, encompassing comparative studies of attention mechanisms, ablation tests, and evaluations against other leading algorithms. The findings indicate that the MFF-YOLOv7 model substantially surpasses other models across various metrics, demonstrates superior underwater target detection capabilities, exhibits enhanced generalization potential, and offers a more dependable and precise solution for underwater target identification.

Advanced Design Validation Planning Techniques for Commercial Vehicle E-Axles

<div>Design validation plays a crucial role in the overall cost and time allocation for product development. This is especially evident in high-value manufacturing sectors like commercial vehicle electric drive systems or e-axles, where the expenses related to sample procurement, testing complexity, and diverse requirements are significant.</div> <div>Validation methodologies are continuously evolving to encompass new technologies, yet they must be rigorously evaluated to identify potential efficiencies and enhance the overall value of validation tests. Simulation tools have made substantial advancements and are now widely utilized in the development phase. The integration of simulation-based or simulation-supported validation processes can streamline testing timelines and sample quantities, all the while upholding quality standards and minimizing risks when compared to traditional methods. This study examines various scenarios where the implementation of advanced techniques has led to a reduction in the e-axle design validation plan (DVP), particularly in relation to the start of production (SOP) decision-making quality gates.</div> <div>Many DVPs incorporate tests in a specific order to enhance the validation impact of each sample and to guarantee that preconditioning tests are implemented when necessary. An instance of preconditioning is subjecting a system to thermal cycling. Thermal cycle preconditioning ensures that the sample accurately represents a real-world system when undergoing sealing-related tests, such as ingress protection testing according to ISO 20653 [<span>1</span>]. Assessing the necessary thermal cycling to “bed-in” a sample would be highly elaborate using a physical sample, but it is a straightforward aspect to extract from modern finite element analysis (FEA). A case study will be presented to evaluate the minimum required thermal cycling and determine the cost savings for the DVP.</div> <div>Furthermore, the validation supported by simulations will be showcased in the context of accelerated lifetime testing. Often, accelerating such tests necessitates a balance between the distribution of damages and the overall testing duration. By incorporating safety factors from simulations as an initial input, the distribution of damages can be adjusted to allow for some over-testing on more secure components, thereby reducing the overall testing time.</div> <div>Efficiency assessment will additionally be considered for virtual validation due to the complexity of the measurement and data acquisition, contributing to measurement uncertainty. Furthermore, simulation-based validation will be considered for the case of lubrication testing, which can be hampered by a largely qualitative assessment criteria.</div> <div>Overall, these scenarios will be evaluated for their potential to streamline the overall DVP process in terms of testing time, sample quantity, and critical path duration. This underscores the potential benefits of leveraging advanced DVP techniques for cost and time savings.</div>

MiRNA in archival serum samples derived from breast cancer patients treated with neoadjuvant chemotherapy vs freshly collected samples: Pilot study.

Liquid biopsy, including miRNA profiling, is a promising approach to identify breast cancer (BC) resistance. However, the effect of long-term storage on the quality of miRNA assessment in archival serum has not been fully addressed. We aimed to determine whether miRNAs were recoverable from long-stored serum samples to subsequently evaluate prognostic and predictive miRNA value in the archival collection of samples from patients treated with neoadjuvant chemotherapy at Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice, Poland. We have evaluated miRNA quantity in serum samples stored for up to 12 years. Additionally, we compared miRNA expression in archival samples to freshly collected samples derived from advanced BC patients. Forty BC patients were included in the study. Archival samples were derived from 20 BC patients treated with radical intent between 2011 and 2015. Freshly collected samples were collected from 20 advanced BC patients in 2022. miRNA was present in archived serum samples frozen at -80C° for at least 12 years. Additionally, we found significantly different expressions between the 2 analyzed groups. Expression of circulating miR-16, -17, -18a, -20a, -21, -27a, -30b, -222, and -326 were significantly higher in archival samples, whereas expression of circulating miR-19a, -29b, -29c, -128, -145, -146a, -193b, -195, -200b, -210, -221, -424, and -451a were lower than in freshly collected samples. In 14 miRs, we observed expression in both groups; however, differences were statistically insignificant (miR-1, -7a, -10b, -19b, -34a, -99a, -106b, -122, -125b, -155, -200a, -205, -223, -340). MiRNA can be identified from long-stored samples, making large prospectively collected serum repositories with long follow-up time an invaluable source for miRNA biomarker discovery.

Enantiomerically Pure Helical Bilayer Nanographenes: A Straightforward Chemical Approach.

The semiconductor properties of nanosized graphene fragments, known as molecular nanographenes, position them as exceptional candidates for next-generation optoelectronics. In addition to their remarkable optical and electronic features, chiral nanographenes exhibit high dissymmetry factors in circular dichroism and circularly polarized luminescence measurements. However, the synthesis of enantiomerically pure nanographenes remains a significant challenge. Typically, these materials are synthesized in their racemic form, followed by separation of the enantiomers using high-performance liquid chromatography (HPLC). While effective, this method often requires expensive instrumentation, extensive optimization of separation conditions, and typically yields analytical quantities of the desired samples. An alternative approach is the enantioselective synthesis of chiral molecular nanographenes; however, to date, only two examples have been documented in the literature. In this work, we present a straightforward chemical method for the chiral resolution of helical bilayer nanographenes. This approach enables the effective and scalable preparation of enantiomerically pure nanographenes while avoiding the need for HPLC. The incorporation of a BINOL core into the polyarene precursor facilitates the separation of diastereomers through esterification with enantiomerically pure camphorsulfonyl chloride. Following the separation of the diastereomers by standard chromatographic column, the hydrolysis of the camphorsulfonyl group yields enantiomerically pure nanographene precursors. The subsequent graphitization, achieved through the Scholl reaction, occurs in an enantiospecific manner and with the concomitant formation of a furan ring and a heterohelicene moiety. The absolute configurations of the final enantiomers, P-oxa[9]HBNG and M-oxa[9]HBNG, have been determined using X-ray diffraction. Additionally, electrochemical, photophysical, and chiroptical properties have been thoroughly evaluated.

Determination of the pKa and Concentration of NMR-Invisible Molecules and Sites Using NMR Spectroscopy.

NMR spectroscopy is a very powerful tool for measuring the dissociation constants (pKa) of molecules, requiring smaller quantities of samples of lower purity relative to potentiometric or conductometric methods. However, current approaches are generally limited to those molecules possessing favorable pH-dependent NMR properties. Typically, a series of 1D experiments at varying pH are performed, and the pKa is obtained by fitting the observed chemical shift of the analyte as a function of pH using nonlinear routines. However, the majority of polymers, biomolecules, and inorganic species do not present favorable NMR resonances. Either the resonances are not observable or too broad, or the unambiguous interpretation of the NMR data is impossible without resorting to complex 2D experiments due to spectral overlap. To overcome these fundamental limitations, we present a method to obtain the pKa values and concentrations of acidic species without their direct observation by NMR. We instead determine the quantity of acidic protons removed from the species along a concentration gradient of an organic base in a single 1H chemical shift imaging experiment that can be run under automation. The pKa values are determined via simple linear plots, avoiding complex and potentially unreliable nonlinear fitting routines.

Comparison of laser diffractometry and pipetting methods for particle size determination: A pilot study on the implications of result discrepancies on soil classification

AbstractIn recent decades, the determination of particle size distribution (PSD) using the laser diffraction method (LDM) has become increasingly common, supplanting traditional sedimentation techniques. Advances in everything from sample preparation to software settings have been realized globally, whether through recommendations from laser diffraction (LD) manufacturers or through user experiences. These developments seek to enhance accuracy and diminish the uncertainties associated with new methodologies. Particularly in the determination of PSD using LDM on various LD instruments and in comparison with the sieve–pipette method (SPM), discrepancies in PSD frequently arise. This article aims to mitigate these discrepancies by predefining parameters, specifically through the adjustment of LD software settings and sample preparation (employing a uniform set of dispersed samples in potassium hydroxide) on two widely used LD instruments for soil measurements: Mastersizer 3000 and Analysette 22. Additionally, these samples were analyzed using the traditional SPM (ISO 11277, 1998), with the results from LDM and SPM subsequently compared. The paper also explores the impact, range of user options, and limitations of predefined software settings on both LD instruments. Eighty soil samples were analyzed for PSD, collected from arable land in the cadastral area of Hrušky, district of Břeclav (Czech Republic), in spring 2022, from depths of 0‐ to 10‐cm and 10‐ to 20‐cm. Significant differences in PSD were confirmed, although the trends of the grain size distribution curves were very similar to those of LDM. Results from the Mastersizer underestimated the clay fraction by an average of 17% compared to SPM, at the expense of the sand fraction, whereas the silt fraction was underestimated by a maximum of 4%. Conversely, Analysette 22 overestimated the silt fraction by an average of 37% at the expense of the sand fraction, confirming only a slight difference in the clay fraction: 3%. Moreover, the quantity of sample entering the dispersion unit was identified as a significant issue when comparing LD instruments, despite the obscuration value being nearly identical, that is, 20%–30%. Therefore, it was not possible to achieve the same or similar weight when introducing suspension into circulation. The robustness of the obtained results underscores the importance of understanding input parameters when interpreting results between different methods.

Potential role of bacterial extracellular vesicles in the pathophysiology of systemic lupus erythematosus

Systemic Lupus Erythematosus (SLE) is a rare autoimmune disorder influenced by various factors, including infections. Following bacterial or viral infections, there is an increase in IFN-I production, primarily by plasmacytoid dendritic cells (pDCs). Overproduction of IFN-I has been shown to drive the progression of SLE. Recent findings indicate that the gene expression signature of IFN-I in lupus nephritis patients does not co-occur with pDCs as expected; instead, it is localized in glomerular regions with anastomosing capillaries, suggesting a potential source from the blood. T cells, natural killer cells, and myeloid lineage cells may also secrete IFN in the bloodstream. Bacterial presence in the bloodstream challenges the concept of blood sterility, raising the possibility that cell-free microbial components, such as bacterial extracellular vesicles (BEVs), could trigger excessive IFN production through systemic circulation. While existing studies suggest a role for BEVs in human SLE, experimental evidence has yet to establish a direct association between the entire BEV nanoparticle and the disease. Research has primarily focused on (1) mammalian EVs and (2) purified eDNA and other specific cargoes as contributors to SLE progression. It remains unproven whether these bacterial molecules exert their effects while associated with vesicular membranes, i.e., BEVs. We hypothesize that BEVs, which carry diverse cargoes including nucleic acids, may enter systemic circulation, induce cellular responses leading to IFN overproduction, and exacerbate SLE severity. Clinical studies could investigate the association of BEVs with SLE progression by monitoring their presence and quantity in blood samples and correlating these factors with disease outcomes. Furthermore, understanding the involvement of BEVs may aid in identifying SLE biomarkers, inform infection prevention strategies, and inspire the development of BEV-neutralizing agents.

Screening for Microplastics in Agricultural Soils: Applying Green Chemistry Principles in Extraction and Analysis.

In recent years, microplastic (MP) pollution has garnered significant attention owing to its ability to permeate various ecosystems, including soil. These particles can infiltrate the environment, either directly or through the degradation of larger plastic items. Despite growing concerns, standardized methods for quantification are still lacking. This study aimed to screen for the presence of MPs in agricultural soils while incorporating green analytical principles in the methodology. A density separation followed by centrifugation was employed, based on the principles of the QuEChERS extraction method. This approach minimized sample quantities, reagent consumption, and waste production, ensuring efficient extraction and analysis. Recovery tests using certified soils spiked with pristine MPs, specifically polystyrene, polypropylene (PP), and ethylene-vinyl acetate for larger MPs (3-5 mm), and low-density polyethylene, polyamide 6, and tire wear particles for smaller MPs (15-300 μm),achieved recovery levels exceeding 69% for smaller MPs and over 91% for larger particles. Spectroscopic analysis revealed slight alterations in the Raman spectra of MPs after extraction. Transitioning to agricultural soil analysis has revealed challenges, including spectral interferences. Nine mesoplastics (plastic items 5-20 mm) were detected, predominantly consisting of PP and PE, along with seven MPs, three of which were individually identified as PE-based, while the remainder were inconclusive, including one fiber. The evaluation of the method's sustainability using the Analytical Eco-Scale and Analytical Greenness Calculator Metric (AGREE), with scores of 82 out of 100 and 0.66 out of 1, demonstrated its potential as a liable approach to MP analysis in soils. This study highlights the potential of integrating green analytical chemistry principles into MP extraction methodologies and emphasizes the value of the proposed QuEChERs-based approach for improving the sustainability and efficiency of MP monitoring in agricultural soils.

Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification

In Hyperspectral Image (HSI) classification, acquiring large quantities of high-quality labeled samples is typically costly and impractical. Traditional deep learning methods are limited in such scenarios due to their dependence on sample quantities. To address this challenge, researchers have turned to Few-Shot Learning (FSL). Although existing FSL methods improve classification performance by enhancing domain invariance through domain adaptation, they often overlook the critical issue of high inter-class similarity and large intra-class variability. Moreover, during domain alignment, features of different categories within the same domain may become confused. To address these issues, this paper proposes a novel Domain-Invariant Few-Shot Contrastive Learning (DIFSCL) method, which combines domain adaptation and contrastive learning strategies to not only learn domain-invariant features but also significantly enhance inter-class discriminability. Based on this, we further design a multi-scale adaptive attention mechanism for a hyperspectral feature extraction network to more effectively extract and optimize generalized features within the DIFSCL framework, significantly improving intra-class consistency and inter-class discriminative capability of features. Experimental results on three widely used HSI datasets demonstrate that our method significantly outperforms existing techniques in few-shot classification tasks.

Efficient generation of HPLC and FTIR data for quality assessment using time series generation model: a case study on Tibetan medicine Shilajit.

The scarcity and preciousness of plateau characteristic medicinal plants pose a significant challenge in obtaining sufficient quantities of experimental samples for quality evaluation. Insufficient sample sizes often lead to ambiguous and questionable quality assessments and suboptimal performance in pattern recognition. Shilajit, a popular Tibetan medicine, is harvested from high altitudes above 2000m, making it difficult to obtain. Additionally, the complex geographical environment results in low uniformity of Shilajit quality. To address these challenges, this study employed a deep learning model, time vector quantization variational auto- encoder (TimeVQVAE), to generate data matrices based on chromatographic and spectral for different grades of Shilajit, thereby increasing in the amount of data. Partial least squares discriminant analysis (PLS-DA) was used to identify three grades of Shilajit samples based on original, generated, and combined data. Compared with the originally generated high performance liquid chromatography (HPLC) and Fourier transform infrared spectroscopy (FTIR) data, the data generated by TimeVQVAE effectively preserved the chemical profile. In the test set, the average matrices for HPLC, FTIR, and combined data increased by 32.2%, 15.9%, and 23.0%, respectively. On the real test data, the PLS-DA model's classification accuracy initially reached a maximum of 0.7905. However, after incorporating TimeVQVAE-generated data, the accuracy significantly improved, reaching 0.9442 in the test set. Additionally, the PLS-DA model trained with the fused data showed enhanced stability. This study offers a novel and effective approach for researching medicinal materials with small sample sizes, and addresses the limitations of improving model performance through data augmentation strategies.

Mapping Active RNA Polymerases in Proliferating and Quiescent Fission Yeast Cells Using Precision Run-On Sequencing.

The development of next-generation sequencing (NGS) approaches to investigate the functioning of RNA polymerases has led to groundbreaking advances in the field of transcriptional regulation. One powerful method, Precision nuclear Run-On sequencing (PRO-seq), maps the locations of RNA polymerase active sites genome-wide at high resolution. PRO-seq provides a snapshot of strand-specific transcriptional activity and does not rely on immunoprecipitation of the polymerase of interest. Notably, this technique has been utilized to investigate the control of the RNA polymerase II transcription cycle in a variety of model systems. However, the initially published PRO-seq method required significant amounts of starting sample and was technically challenging, both of which were deterrents for its broader use. Recently, an improved and simplified version called qPRO-seq that reduced the length of the experiment and the quantity of necessary input sample was developed for human and Drosophila cell lines. Here we provide an updated, step-by-step protocol in which we have validated and optimized qPRO-seq for the fission yeast Schizosaccharomyces pombe. Importantly, we have implemented this method for assessing RNA polymerase activity in nutrient-limiting conditions, for both proliferating and nitrogen-depleted quiescent cells.

Highly specific detection of ROR1 cancer biomarker with bipolar electrochemiluminescence.

An electrochemiluminescence (ECL) detection system is presented integrated with a bipolar electrode system for sensitive cancer diagnosis. In order to achieve the highest electrical conductivity and redox-active surface area, MXene was chosen as the material for the bipolar electrode. As part of the detection process, the anodic pole of the bipolar electrode was modified with the receptor tyrosine kinase like orphan receptor 1 (ROR1) antibody, followed by an immunoassay using the ROR1 antibody-modified Ag triangle that was identified as significantly enhancing ECL. We measured the ECL of luminol using the anode pole of BPE as an analytical signal in the presence of H2O2. Additionally, 3D-printed microchannels were used to fabricate the BPE system, to reduce the quantity of sample required. It has been shown that the present immunosensors are low-cost and sensitive in detecting types of cancer, with an extended linear range of 10 fg mL-1 to 1 µg mL-1 in the analysis of synthetic samples and achieving an accuracy of ~ 90% in diagnosing ten unknown real samples.

Ammonothermal Synthesis of Luminescent Imidonitridophosphate Ba4P4N8(NH)2:Eu2.

The structural variability of a compound class is an important criterion for the research into phosphor host lattices for phosphor-converted light-emitting diodes (pc-LEDs). Especially, nitridophosphates and the related class of imidonitridophosphates are promising candidates. Recently, the ammonothermal approach has opened a systematic access to this substance class with larger sample quantities. We present the successful ammonothermal synthesis of the imidonitridophosphate Ba4P4N8(NH)2:Eu2+. Its crystal structure is solved by X-ray diffraction and it crystallizes in space group Cc (no. 9) with lattice parameters a=12.5250(3), b=12.5566(4), c=7.3882(2) Å and β=102.9793(10)°. For the first time, adamantane-type (imido)nitridophosphate anions [P4N8(NH)2]8- are observed next to metal ions other than alkali metals in a compound. The presence of imide groups in the structure and the identification of preferred positions for the hydrogen atoms are performed using a combination of quantum chemical calculations, Fourier-transform infrared, and solid-state NMR spectroscopy. Eu2+ doped samples exhibit cyan emission (λmax=498 nm, fwhm=50 nm/1981 cm-1) when excited with ultraviolet light with an impressive internal quantum efficiency (IQE) of 41 %, which represents the first benchmark for imidonitridophosphates and is promising for potential industrial application of this compound class.

Actionable Structural Variant Detection via RNA-NGS and DNA-NGS in Patients With Advanced Non–Small Cell Lung Cancer

The National Comprehensive Cancer Network (NCCN) guidelines for non-small cell lung cancer suggest that RNA next-generation sequencing (NGS) may improve the detection of fusions and splicing variants compared with DNA-NGS alone. However, there is limited adoption of RNA-NGS in routine oncology clinical care today. To analyze clinical evidence from a diverse cohort of patients with advanced lung adenocarcinoma and compare the detection of NCCN-recommended actionable structural variants (aSVs; fusions and splicing variants) via concurrent DNA and RNA-NGS vs DNA-NGS alone. This multisite, retrospective cohort study examined patients sequenced between February 2021 and October 2023 within the deidentified, Tempus multimodal database, consisting of linked molecular and clinical data. Participants included patients with advanced lung adenocarcinoma and sufficient tissue sample quantities for both RNA-NGS and DNA-NGS testing. Received results from RNA-NGS and DNA-NGS solid-tissue profiling assays. Detection rates of NCCN guideline-based structural variants (ALK, ROS1, RET and NTRK1/2/3 fusions, as well as MET exon 14 skipping splicing alterations) found uniquely by RNA-NGS. In the evaluable cohort of 5570 patients, median (IQR) age was 67.8 (61.3-75.4) years, and 2989 patients (53.7%) were female. The prevalence of actionable structural variants detected by either RNA-NGS or DNA-NGS was 8.8% (n = 491), with 86.7% (n = 426) of these detected by DNA-NGS. Concurrent RNA-NGS and DNA-NGS identified 15.3% more patients harboring aSVs compared with DNA-NGS alone (491 vs 426 patients, respectively), including 14.3% more patients harboring actionable fusions (376 vs 329 patients) and 18.6% more patients harboring MET exon 14 skipping alterations (115 vs 97 patients). There was no significant association between the assay used for aSV detection and aSV-targeted therapeutic adoption or clinical outcome. Emerging structural variants (eSVs) were found to have a combined prevalence to be 0.7%, with only 47.5% of eSVs detected by DNA-NGS. In this cohort study, the detection of structural variants via concurrent RNA-NGS and DNA-NGS was higher across multiple NCCN-guideline recommended biomarkers compared with DNA-NGS alone, suggesting that RNA-NGS should be routinely implemented in the care of patients with advanced NSCLC.

Mass Spectrometry Probe Combined with Machine Learning to Capture the Relationship between Metabolites and Mitochondrial Complex Activity at the Whole-Cell Level.

Mitochondrial complex activity controls a multitude of physiological processes by regulating the cellular metabolism. Current methods for evaluating mitochondrial complex activity mainly focus on single metabolic reactions within mitochondria. These methods often require fresh samples in large quantities for mitochondria purification or intact mitochondrial membranes for real-time monitoring. Confronting these limitations, we shifted the analytical perspective toward interactive metabolic networks at the whole-cell level to reflect mitochondrial complex activity. To this end, we compiled a panel of mitochondrial respiratory chain-mapped metabolites (MRCMs), whose perturbations theoretically provide an overall reflection on mitochondrial complex activity. By introducing N-dimethyl-p-phenylenediamine and N-methyl-p-phenylenediamine as a pair of mass spectrometry probes, an ultraperformance liquid chromatography-tandem mass spectrometry method with high sensitivity (LLOQ as low as 0.2 fmol) was developed to obtain accurate quantitative data of MRCMs. Machine learning was then combined to capture the relationship between MRCMs and mitochondrial complex activity. Using Complex I as a proof-of-concept, we identified NADH, alanine, and phosphoenolpyruvate as metabolites associated with Complex I activity based on the whole-cell level. The effectiveness of using their concentrations to reflect Complex I activity was further validated in external data sets. Hence, by capturing the relationship between metabolites and mitochondrial complex activity at the whole-cell level, this study explores a novel analytical paradigm for the interrogation of mitochondrial complex activity, offering a favorable complement to existing methods particularly when sample quantities, type, and treatment timeliness pose challenges. More importantly, it shifts the focus from individual metabolic reactions within mitochondria to a more comprehensive view of an interactive metabolic network, which should serve as a promising direction for future research into the functional architecture between mitochondrial complexes and metabolites.

Versatility of Raman spectroscopy for studies on the back-end of the nuclear fuel cycle

In recent years, Raman spectroscopy has been proven to be a highly versatile characterization technique for nuclear materials research. This sensitive technique possesses, among others, two relevant features that comply with the safety conditions required when handling nuclear materials: its flexibility allows for remote, in situ and ex situ analysis, and its relative ease of use implies small sample quantities and limited effort for sample preparation. In this work, we present the acquisition protocol and data processing necessary for obtaining important information within the back-end of the nuclear fuel cycle. Specifically, we focus on research studies on the advanced characterization of nuclear fuels and the development of hydrometallurgical separation processes. The results described here were obtained by using different nuclear fuels analogs, but the acquisition protocol and data processing described can be applied to the real nuclear fuel cycle.Graphical abstract

The Impact of Sample Quantity, Traceability Scale, and Shelf Life on the Determination of the Near-Infrared Origin Traceability of Mung Beans.

This study aims to address the gap in understanding of the impact of the sample quantity, traceability range, and shelf life on the accuracy of mung bean origin traceability models based on near-infrared spectroscopy. Mung beans from Baicheng City, Jilin Province, Dorbod Mongol Autonomous, Tailai County, Heilongjiang Province, and Sishui County, Shandong Province, China, were used. Through near-infrared spectral acquisition (12,000-4000 cm-1) and preprocessing (Standardization, Savitzky-Golay, Standard Normal Variate, and Multiplicative Scatter Correction) of the mung bean samples, the total cumulative variance contribution rate of the first three principal components was determined to be 98.16% by using principal component analysis, and the overall discriminatory correctness of its four origins combined with the K-nearest neighbor method was 98.67%. We further investigated how varying sample quantities, traceability ranges, and shelf lives influenced the discrimination accuracy. Our results indicated a 4% increase in the overall correct discrimination rate. Specifically, larger traceability ranges (Tailai-Sishui) improved the accuracy by over 2%, and multiple shelf lives (90-180-270-360 d) enhanced the accuracy by 7.85%. These findings underscore the critical role of sample quantity and diversity in traceability studies, suggesting that broader traceability ranges and comprehensive sample collections across different shelf lives can significantly improve the accuracy of origin discrimination models.