Amount Of Sample Research Articles

Mask-guided BERT for few-shot text classification

Transformer-based language models have achieved significant success in various domains. However, the data-intensive nature of the transformer architecture requires much labeled data, which is challenging in low-resource scenarios (i.e., few-shot learning (FSL)). The main challenge of FSL is the difficulty of training robust models on small amounts of samples, which frequently leads to overfitting. Here we present Mask-BERT, a simple and modular framework to help BERT-based architectures tackle FSL. The proposed approach fundamentally differs from existing FSL strategies such as prompt tuning and meta-learning. The core idea is to selectively apply masks on text inputs and filter out irrelevant information, which guides the model to focus on discriminative tokens that influence prediction results. In addition, to make the text representations from different categories more separable and the text representations from the same category more compact, we introduce a contrastive learning loss function. Experimental results on open-domain and medical-domain datasets demonstrate the effectiveness of Mask-BERT. Code and data are available at: github.com/WenxiongLiao/mask-bert

Rapid Tryptophan Assay as a Screening Procedure for Quality Protein Maize.

Tryptophan is an essential amino acid deficient in cereals, especially maize. However, maize (Zea mays L.) is the main source of protein in some developing countries in Africa and Latin America. In general, the nutritional profile of cereals is poor, because they are deficient in essential amino acids such as tryptophan and lysine due to a relatively higher proportion of alcohol-soluble proteins. Quality protein maize (QPM) has been developed through genetic manipulation for the nutritional enrichment of maize to address these problems. Nevertheless, methods for protein, lysine and tryptophan are time-consuming and require relatively large amounts of samples. Therefore, we have advanced here a simple, cheap, fast, reliable and robust procedure for the determination of protein and tryptophan in the same biuret supernatant, which can also be used for chemical characterization of other cereals. Samples of 50 mg maize ground to pass through a 0.1 mm screen were sonicated for 5 min. in eppendorf vials with 1.5 mL of a biuret reagent each. After centrifugation and protein determination by biuret, 0.2 mL of supernatant was treated with 0.8 mL of a tryptophan reagent. Both total protein and tryptophan can be determined in microplates at 560 nm to speed up the measurements. The main advantage of the new micro-method is the rapid estimation of the nutrient quality of maize samples by a single weighing of a small amount of valuable plant materials.

Review and Evaluate the Bioinformatics Analysis Strategies of ATAC-seq and CUT&Tag Data.

Efficient and reliable profiling methods are essential to study epigenetics. Tn5, one of the first identified prokaryotic transposases with high DNA-binding and tagmentation efficiency, is widely adopted in different genomic and epigenomic protocols for high-throughputly exploring the genome and epigenome. Based on Tn5, the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and the Cleavage Under Targets and Tagmentation (CUT&Tag) were developed to measure chromatin accessibility and detect DNA-protein interactions. These methodologies can be applied to large amounts of biological samples with low-input levels, such as rare tissues, embryos, and sorted single cells. However, fast and proper processing of these epigenomic data has become a bottleneck because massive data production continues to increase quickly. Furthermore, inappropriate data analysis can generate biased or misleading conclusions. Therefore, it is essential to evaluate the performance of Tn5-based ATAC-seq and CUT&Tag data processing bioinformatics tools, many of which were developed mostly for analyzing chromatin immunoprecipitation followed by sequencing (ChIP-seq) data. Here, we conducted a comprehensive benchmarking analysis to evaluate the performance of eight popular software for processing ATAC-seq and CUT&Tag data. We compared the sensitivity, specificity, and peak width distribution for both narrow-type and broad-type peak calling. We also tested the influence of the availability of control IgG input in CUT&Tag data analysis. Finally, we evaluated the differential analysis strategies commonly used for analyzing the CUT&Tag data. Our study provided comprehensive guidance for selecting bioinformatics tools and recommended analysis strategies, which were implemented into Docker/Singularity images for streamlined data analysis.

Multi-step HPLC fractionation enabled in-depth and unbiased characterization of histone PTMs

Histone post-translational modifications (PTMs) are critical epigenetic regulatory factors. Histone PTMs are highly dynamic and complicated, encompassing over 30 structurally diverse modifications across nearly 180 amino acid residues, which generated extensive information regarding histone marks. In proteomics-based characterization of histone PTMs, chemical derivatization and antibody-based affinity enrichment were frequently utilized to improve the identification depth. However, chemical derivatization suffered from the occurrence of side reactions, and antibody-based affinity enrichment focused on specific PTM types of interest. In this research, we developed a multi-step fractionation strategy for comprehensively unbiased detection of histone PTM sites. By combining protein-level fractionation with peptide-level alkaline and acid phase fractionation, we developed the Multidimensional Fractionation based Histone Mark Identification Technology (MudFIT) and increased PTM identification to a total of 264 histone PTM sites. To the best of our knowledge, this strategy achieved the most comprehensive characterization of histone PTM sites in a single proteomics study. Using the same starting amount of sample, MudFIT identified more Kac sites and Kac peptides than those in antibody-based acetylated peptide enrichment. Moreover, in addition to well-studied histone marks, we discovered 36 potential new histone PTM sites including H2BK116bu, H4R45me2, H1K63pr, and uncovered unknown histone PTM types like aminoadipic on lysine and nitrosylation on tyrosine. Our data provided a method and resource for in-depth characterization of histone PTM sites, facilitating further biological understanding of histone marks.

Heat capacity measurements by a Setaram μDSC3 evo microcalorimeter: estimation of deviation in the measurement, advanced data analysis by mathematical gnostics, and prediction by the artificial neural network

AbstractThe aim of the work is to study the variation in the isobaric heat capacity measurement due to changes in the amount of sample and the calibration standard using a Setaram $$\mu$$ μ DSC3 evo microcalorimeter batch cells to provide a guideline toward the selection of the sample amount to minimize heat capacity measurement error in $$\mu$$ μ DSC. Moreover, overall variation, variation due to the sample amount, and variation due to the calibration standard (reference) amount in heat capacity measurement were estimated for different amounts of the sample or/and the calibration standard material. In the present work, heat capacity measurements were taken for [C4mim][Tf2N] (1-butyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide) ionic liquid as a sample material and 1-butanol as a calibration standard. A novel non-statistical approach, mathematical gnostics (MG), was used for data analysis of measured heat capacities data. Moreover, the artificial neural network (ANN) model was developed to predict the deviation in the heat capacity measurement with 99.83% accuracy and 0.9939 R2 score. The Python package PyCpep based on the trained ANN model was developed to predict the deviation in the heat capacity measurement.

Microvolumetric determination of thrombomodulin based on competitive immunoreaction using a portable glucometer.

A new method of reducing the amount of reagent and sample for determination of thrombomodulin (TM) was developed based on competitive immunoreaction using a portable glucometer (PGM). Two types of nanocomposites, TM protein-modified magnetic nanoparticles (MNPs-TM) and TM antibody-/glucose oxidase-modified gold nanoparticles (Ab-GNPs-GOx), were prepared. Their binding product, MNPs-TM-Ab-GNPs-GOx, in the microvolumetric solution was used to catalyze the oxidation of glucose, leading toa decline of the glucose content. The TM-involved competitive immunoreaction had a negative effect on the generation of MNPs-/GNPs-based nanocomposites and inhibited the catalytic oxidation of glucose. The glucose content difference in the microvolumetric solution, which was revealed by a PGM, was in proportion to the logarithm of the TM concentration from 25ngmL-1 to 2.5μgmL-1. The limit of detection was 5.7ngmL-1. Microvolumetric solution and a PGM were used in the measurement, which overcame some deficienciesof classical methods in chemo/biosensing, for example, special instrument, complicated measurement procedure, and high cost.

Coarse-to-Fine Structure and Semantic Learning for Single-Sample SAR Image Generation

Synthetic Aperture Radar (SAR) enables the acquisition of high-resolution imagery even under severe meteorological and illumination conditions. Its utility is evident across a spectrum of applications, particularly in automatic target recognition (ATR). Since SAR samples are often scarce in practical ATR applications, there is an urgent need to develop sample-efficient augmentation techniques to augment the SAR images. However, most of the existing generative approaches require an excessive amount of training samples for effective modeling of the SAR imaging characteristics. Additionally, they show limitations in augmenting the interesting target samples while maintaining image recognizability. In this study, we introduce an innovative single-sample image generation approach tailored to SAR data augmentation. To closely approximate the target distribution across both the spatial layout and local texture, a multi-level Generative Adversarial Network (GAN) architecture is constructed. It comprises three distinct GANs that independently model the structural, semantic, and texture patterns. Furthermore, we introduce multiple constraints including prior-regularized noise sampling and perceptual loss optimization to enhance the fidelity and stability of the generation process. Comparative evaluations against the state-of-the-art generative methods demonstrate the superior performance of the proposed method in terms of generation diversity, recognizability, and stability. In particular, its advantages over the baseline method are up to 0.2 and 0.22 in the SIFID and SSIM, respectively. It also exhibits stronger robustness in the generation of images across varying spatial sizes.

Clinical metabolomics: current state and prospects in Russia.

Using analytical technologies it is possible now to measure the entire diversity of molecules even in a small amount of biological samples. Metabolomic technologies simultaneously analyze thousands of low-molecular substances in a single drop of blood. Such analytical performance opens new possibilities for clinical laboratory diagnostics, still relying on the measurement of only a limited number of clinically significant substances. However, there are objective difficulties hampering introduction of metabolomics into clinical practice. The Institute of Biomedical Chemistry (IBMC), consolidating the efforts of leading scientific and medical organizations, has achieved success in this area by developing a clinical blood metabogram (CBM). CBM opens opportunities to obtain overview on the state of the body with the detailed individual metabolic characteristics of the patient. A number of scientific studies have shown that the CBM is an effective tool for monitoring the state of the body, and based on the CBM patterns (signatures), it is possible to diagnose and monitor the treatment of many diseases. Today, the CBM creation determines the current state and prospects of clinical metabolomics in Russia. This article, dedicated to the 80th anniversary of IBMC, is a review of these achievements focused on a discussion of their implementation in clinical practice.

MicroLOCK: Highly stable microgel biosensor using locked nucleic acids as bioreceptors for sensitive and selective detection of let-7a

Chemically modified oligonucleotides can solve biosensing issues for the development of capture probes, antisense, CRISPR/Cas, and siRNA, by enhancing their duplex-forming ability, their stability against enzymatic degradation, and their specificity for targets with high sequence similarity as microRNA families. However, the use of modified oligonucleotides such as locked nucleic acids (LNA) for biosensors is still limited by hurdles in design and from performances on the material interface. Here we developed a fluorogenic biosensor for non-coding RNAs, represented by polymeric PEG microgels conjugated with molecular beacons (MB) modified with locked nucleic acids (MicroLOCK). By 3D modeling and computational analysis, we designed molecular beacons (MB) inserting spot-on LNAs for high specificity among targets with high sequence similarity (95%). MicroLOCK can reversibly detect microRNA targets in a tiny amount of biological sample (2μL) at 25°C with a higher sensitivity (LOD 1.3fM) without any reverse transcription or amplification. MicroLOCK can hybridize the target with fast kinetic (about 30min), high duplex stability without interferences from the polymer interface, showing high signal-to-noise ratio (up to S/N=7.3). MicroLOCK also demonstrated excellent resistance to highly nuclease-rich environments, in real samples. These findings represent a great breakthrough for using the LNA in developing low-cost biosensing approaches and can be applied not only for nucleic acids and protein detection but also for real-time imaging and quantitative assessment of gene targeting both in vitro and in vivo.

Recent advances in capillary electrophoresis-mass spectrometry analysis of ‍trace biomolecules

In recent years, various trace bioanalysis methods have been developed, including single-cell transcriptome analysis methods. As the sample volume and amount of biomolecules contained therein are extremely limited, development of new single-cell analysis methods require extremely high-level techniques. It is necessary to design an appropriate analysis system that integrates a highly sensitive detection system and a pretreatment protocol for minimizing sample loss, where separation method is especially important for analyzing diverse mixtures of biomolecules. Among them, capillary electrophoresis (CE) can separate biomolecules in nanoliter-scale solutions with high resolution, making it highly compatible with trace samples such as single cells. By combining with highly sensitive nano-electrospray ionization-mass spectrometry (MS), it is possible to detect nanomolar to sub-nanomolar biomolecules, which can be further improved by using online sample preconcentration methods. These highly sensitive analytical techniques have made it possible to analyze trace amounts of metabolites, proteins, lipids, etc. This review paper summarizes the research on CE-MS trace bioanalysis that has been reported to date, with a focus on single-cell analysis.

Performance evaluation of endoscopic ultrasonography needles: Experimental study.

Background and study aims The relative procedural performance of needles for endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNA/B) is unclear. The present study therefore compared six types of 22-gauge FNA/B needles using a bench simulator. Methods Resistance forces during needle puncture and removal, needle tip damage before and after procedure, leakage after puncture of mucinous cyst models, the shape of the puncture surface at the puncture site, amounts of samples extracted, ranges of needle deflection angles, and needle deformation after multiple procedures were compared using six types of needles. Results Maximum resistance forces during puncture and removal were highest for ProCore needles and lowest for Expect needles. None of the needles had damage after puncturing. SharkCore needles showed the highest amount of leakage, whereas FNA needles showed no leakage. The puncture tracts of FNA needles remained in the form of a flap at the puncture site, whereas FNB needles broke off the target material creating a hole. The target material removed was supplemented within the puncture needle. TopGain needles produced significantly larger samples than ProCore, EZShot3 Plus, and Expect needles. FNB needles produced larger and more core samples than FNA needles. EZShot3 Plus needles had the highest range of needle deflection angle using an elevator device and the lowest needle deformation after 20 punctures at full endoscopic angle and a full elevator. Conclusions The performance of the six needles differed in various ways. Understanding the characteristics of each needle may allow for selection of the appropriate needle for each situation.

Molecular phylogeny and species diversity of the genus Dichaetophora Duda and related taxa (Diptera: Drosophilidae)

Our intensive surveys of wild drosophilids in East and Southeast Asia discovered a great species diversity (more than 100 putatively new species) of the genus Dichaetophora, which is currently comprised of 67 formally described species assigned into five species groups, i.e., agbo, tenuicauda, acutissima, sinensis and trilobita. In the present study, we delimited species from a huge amount of samples of Dichaetophora and allied taxa (the genus Mulgravea and the subgenus Dudaica of Drosophila) collected from a wide range of the Oriental and east Palearctic regions. We first sorted all specimens into morpho-species, and representative specimen(s) selected from each morpho-species were subjected to barcoding of COI (the cytochrome c oxidase subunit I gene) sequences. The applied ASAP (Assemble Species by Automatic Partitioning) analysis estimated a total of 166 to 168 MOTUs (molecular operational taxonomic units). Integrating this result with morphological evidence from re-examined, detailed structures of male terminalia, we recognized a total of 144 (109 new and 35 known) species in our sample. Out of them, 83 species representing the supraspecific taxa of Dichaetophora, Mulgravea and Dudaica were selected, along with 33 species from major genera and subgenera of Drosophila in the tribe Drosophilini, as in-group and four species from the tribe Colocasiomyini as out-group for phylogenetic reconstruction based on 12 nuclear gene markers. In the trees constructed by the maximum likelihood and Bayesian inference methods, the three focal taxa (i.e., Dichaetophora, Mulgravea and Dudaica) formed a clade provisionally called the “pan-Dichaetophora”. Within this large clade, the agbo, tenuicauda, sinensis and trilobita groups of Dichaetophora, Mulgravea and Dudaica were recovered as monophyletic groups, but Dichaetophora and its acutissima group were regarded as paraphyletic. In addition, two clusters were recognized among ungrouped species of Dichaetophora. Thus, the present study has uncovered some issues concerning the taxonomy of the pan-Dichaetophora. Such issues will be addressed elsewhere in the phylogenetic reclassification of the pan-Dichaetophora, along with descriptions/redescriptions of a large number of new/known species delimited in the present study.

Effect of the Addition of Cu and Al on the Microstructure, Phase Composition and Properties of a Ti-6Al-4V Alloy Obtained by Selective Laser Melting

The present study considers the samples of an Ti-6Al-4V alloy obtained by selective laser melting with the addition of a 10% Cu-Al powder mixture. The microstructure, elemental composition and phase composition, as well as the physico-chemical properties, have been investigated by the methods of electron microscopy, X-ray phase analysis, and bending testing. The obtained samples have a relative density of 98.5 ± 0.1%. The addition of the Cu-Al powder mixture facilitates supercooling during crystallization and solidification, which allows decreasing the size and changing the shape of the initial β-Ti grains. The constant cooling rate of the alloy typical for the SLM technology has been shown to be able to prevent martensitic transformation. The formation of a structure that consists of β-Ti grains, a dispersed eutectoid mixture of α-Ti and Ti2Cu grains, and a solid solution of Al in Cu has been revealed. In the case of doping by the 10% Cu-Al mixture, the physico-mechanical properties are improved. The hardness of the samples amounts to 390 HRC, with the bending strength being 1550 ± 20 MPa and deformation of 3.5 ± 0.2%. The developed alloy can be recommended for applications in the production of parts of jet and car engines, implants for medicine, and corrosion-resistant parts for the chemical industry.

Capillary zone electrophoresis-tandem mass spectrometry for in-depth proteomics analysis via data-independent acquisition.

A capillary zone electrophoresis (CZE) system was coupled to an Orbitrap mass spectrometer operating in a data-independent acquisition (DIA) mode for in-depth proteomics analysis. The performance of this CZE-DIA-MS system was systemically evaluated and optimized under different operating conditions. The performance of the fully optimized CZE-DIA-MS system was subsequently compared to the one by using the same CZE-MS system operating in a data-dependent acquisition (DDA) mode. The experimental results show that the numbers of identified peptides and proteins acquired in the DIA mode are much higher than the ones acquired in the DDA mode, especially with the small sample loading amount. Specifically, the numbers of identified peptides and proteins acquired in the DIA mode are 1.8-fold and 2-fold higher than the ones acquired in the DDA mode by using 12.5 ng Hela digests. The proteins identified in the DIA mode also cover almost all the proteins identified in the DDA mode. In addition, a potential cancer biomarker protein, carbohydrate antigen 125, undetected in the DDA mode, can be easily identified in the DIA mode even with 12.5 ng Hela digests. The performance of the CZE-DIA-MS system for in-depth proteomics analysis with a limited sample amount has been fully demonstrated for the first time through this study.

Modulation optimization when using a splitter pump after the first dimension in comprehensive two- dimensional liquid chromatography

The rapid growth in the use of two dimensional liquid chromatography (2D-LC) applied to the analysis of moderately to highly complex mixtures, has been fueled by continuous improvements in performance and robustness of the instrument components, as well as the ease-of-use of software necessary for controlling the 2D-LC instrument hardware, and analysis of the large data files that result from this type of work.This work has focused on the evaluation of the performance of an online full comprehensive mode (LC×LC), when an active modulation is implemented using a flow splitter pump placed after the 1D effluent. Two different types of splitting pumps were evaluated: a binary ultra-high pressure liquid chromatography (UHPLC) pump and a high precision syringe pump. We analyzed the performance (reproducibility in peak area and retention times and the 2D peak dispersion) as a function of the location of the active pump Before or After the modulation valve, and the influence of connecting tubes (based on internal diameter and length) necessary between the interface, waste, and the splitting pump. The effect on the flow direction on the filling and flushing of the injection loops at the modulation valve was also analyzed for each pump.In this study, we demonstrate that flow-splitting LCxLC assembly can be performed using either a UHPLC binary pump or a simple syringe pump. Flow splitting after the first dimension is a straightforward strategy to: (i) independently select the 1D column and flow rates with respect to the second dimension; (ii) consciously dilute the eluate according to the solvent characteristics of the second dimension, thereby avoiding 2D peak distortions; and (iii) adapt the injected amount to the second column according to the relative concentration of the components in a complex sample. However, careful consideration of the system setup is necessary. It is demonstrated how experimental results can be significantly affected in terms of peak broadening and reproducibility if optimization of the interface is not taken into account.In addition, under the optimized conditions, the reproducibility in peak area and dispersion in the 2D dimension were evaluated as a function of the amount of sample transferred in terms of percentage of filled loop.

A Finger-Actuated Sample-Dosing Capillary-Driven Microfluidic Device for Loop-Mediated Isothermal Amplification.

Loop-mediated isothermal amplification (LAMP) has attracted significant attention for rapid and accurate point-of-care diagnostics. However, integrating sample introduction, lysis, amplification, and detection steps into an easy-to-use, disposable system has so far been challenging. This has limited the uptake of the technique in practical applications. In this study, we developed a colourimetric one-step LAMP assay that combines thermolysis and LAMP reaction, to detect the SARS-CoV-2 virus in nasopharyngeal swab samples from COVID-19-infected individuals. The limit of detection was 500 copies per reaction at 65 °C for 25 min in reaction tubes. Additionally, we developed a finger-operated capillary-driven microfluidic device with selective PVA coating. This finger-actuated microfluidic device could self-dose the required sample amount for the LAMP reaction and inhibit sample evaporation. Finally, we integrated the LAMP assay into the microfluidic device by short-term pre-storage of the LAMP master mix. Using this device, nasopharyngeal swab samples from COVID-19-infected individuals showed positive results at a reaction time of 35 min at 65 °C. This integrated device may be adapted to detect other RNA viruses of interest rapidly.

Uniaxial compressive strength prediction based on measurement while drilling data: A laboratory study

The uniaxial compressive strength is one of the most important basic parameters of rock, which is essential for surrounding rock stability analysis and support scheme design of underground engineering. At present, it is time consuming and costly to take a large amount of core samples for laboratory testing, and the mechanical properties of the cores may be affected by mining disturbance, which can easily lead to inaccurate result. The measurement while drilling (MWD) technology provides a new approach to solve the above challenge. The key to implementing this technology is to establish a correlation model between drilling parameters and rock mechanics parameters. Based on the characteristics of polycrystalline diamond compact (PDC) bits in drilling and rock breakage, this paper analyzes the mechanical state of the bit in breaking rock. A theoretical correlation model between the torque, feed force of the bit and the uniaxial compressive strength of the rock has been developed. To verify the accuracy of the theoretical model, the uniaxial compressive strength of five different types of rocks (red sandstone, green sandstone, limestone, marble and shale) was obtained through laboratory mechanical tests. The torque Mb, feed force Fb and other parameters in the drilling process of these five rocks were tested through the newly developed MWD test system. The correlation between the drilling parameters and the uniaxial compressive strength of rock was established. The results showed that the feed force Fb and torque Mb measured at five different types of rocks indicate a linear increasing trend with an increase in depth of cut h. Meanwhile, a strong linear relationship between the feed force Fb and torque Mb is evident. This paper proposes an MWD-based method to rapidly conduct the in-situ measurement of the uniaxial compressive strength of various rocks.

Improved efficacy behavioral modeling of microwave circuits through dimensionality reduction and fast global sensitivity analysis

Behavioral models have garnered significant interest in the realm of high-frequency electronics. Their primary function is to substitute costly computational tools, notably electromagnetic (EM) analysis, for repetitive evaluations of the structure under consideration. These evaluations are often necessary for tasks like parameter tuning, statistical analysis, or multi-criterial design. However, constructing reliable surrogate models faces several challenges, including the nonlinearity of circuit characteristics and the vast size of the parameter space, encompassing both dimensionality and design variable ranges. Additionally, ensuring the validity of the model across broad geometry/material parameter and frequency ranges is crucial for its utility in design. The purpose of this paper is to introduce an innovative approach to cost-effective and dependable behavioral modeling of microwave passives. Central to our method is a fast global sensitivity analysis (FGSA) procedure, which is devised to identify correlations between design parameters and quantify their impacts on circuit characteristics. The most significant directions identified through FGSA are utilized to establish a reduced-dimensionality domain. Within this domain, the model may be constructed using a limited amount of data samples while capturing a significant portion of the circuit response variability, rendering it suitable for design purposes. The outstanding predictive capability of the proposed model, its superiority over traditional techniques, and its readiness for design applications are demonstrated through the analysis of three microstrip circuits of diverse characteristics.

The use of sugar kelp (Saccharina latissima) as a seasoning for popcorn: An investigation of consumer acceptance, sensory perception and emotional response

Increasing consumer demand for health-promoting sustainable food products has led to interest in seaweed. Sugar kelp, Saccharina latissimi, is a rich source of vitamins, minerals, fibre, antioxidants, and protein, but it is underutilized as a food ingredient. Seaweed has been identified as a possible seasoning and could be used in snack foods. As such, the purpose of this study was to evaluate how consumers would use sugar kelp as a seasoning on popcorn. The consumers (n = 95) evaluated popcorn and then were asked to add sugar kelp seasoning and to reevaluate the popcorn. The amount of popcorn consumed, and the amount of seasoning used were recorded. Furthermore, the consumers evaluated the popcorn with and without sugar kelp using hedonic scales, free comment, generalized Labelled Magnitude Scales, as well as emotional response (using the EsSense25 profile). The consumers, on average, used 2.5 g of seasoning and consumed the same amount of both samples (with and without sugar kelp). The addition of sugar kelp decreased the liking scores, increased the intensity of umami and bitterness in the samples, and led to fishy, seaweed, and pepper flavours being perceived. It also led to the reduction of positive emotions and an increased selection of negative emotions. The consumers identified that they are interested in foods containing seaweed, but based on the results of the study, the addition of sugar kelp seasoning needs to be further explored.

Using long columns to quantify over 9200 unique protein groups from brain tissue in a single injection on an Orbitrap Exploris 480 mass spectrometer

The most exciting advancement in LC-MS/MS-based bottom-up proteomics has centered around enhancing mass spectrometers. Among these, the latest and most advanced mass spectrometer for bottom-up proteomics is the Orbitrap Astral that has the highest scan rate to accelerate throughput and the highest sensitivity to handle a very small amount of peptide samples and to achieve deeper proteomics. However, its affordability remains a challenge for most laboratories. While significant strides have been made in improving mass spectrometry, advancing liquid chromatography (LC) to achieve deeper proteomics has not achieved significant successes since the innovation of Multidimensional Protein Identification Technology (MudPIT) in 2001. To achieve deeper proteomics in a less labor-intensive and more reproducible approach while using a more cost-effective mass spectrometer, such as the Orbitrap Exploris 480, we evaluated trap columns as long as 40 cm and analytical column as long as 600 cm besides sample loading amount, gradient time, and analytical column particle size to enable a fractionation-free method for a single injection to obtain deeper proteomics. The length of trap and analytic columns is the key factor. Using a 30 cm trap column and 250 cm analytical column with other optimized LC conditions, we quantified over 9200 unique protein groups from brain tissue in a single injection using a 24-h gradient on an Orbitrap Exploris 480 mass spectrometer.