Rapid Analysis Research Articles

Rapid Analysis for α-Tocopherol and Its Oxidative Products in the Pisum sativum L. Leaf Using Supercritical Fluid Chromatography-Medium Vacuum Chemical Ionization Tandem Mass Spectrometry.

A method for the rapid determination of α-tocopherol (α-T) and its oxidative products in plant tissue has been developed using supercritical fluid extraction (SFE) coupled with supercritical fluid chromatography (SFC) and medium vacuum chemical ionization (MVCI) with tandem mass spectrometry. The method is designed to study changes in levels for α-T and its oxidative products in plant cells during photosynthesis, aiming to observe the light response curves. α-T oxidation is a non-enzymatic self-defense mechanism in plant cells. Unlike enzyme-involved reactions, it cannot be stopped, so the oxidation continues in crude extracts even after extraction. Therefore, a real-time in-situ method is essential for tracking the light response curves. To optimize the selective reaction monitoring method, the reaction mixture of α-T and singlet oxygen (1O2), generated by rose Bengal under light illumination, was used as the source of oxidative products. The relative abundance changes in α-tocopherylquinone and 8a-hydroperoxy tocopherone in Pisum sativum L. (Pea) leaves under excessive light illumination have been preliminarily analyzed as part of the light response curve study. The method archives a throughput of 10-15 minutes for analyzing duplicate leaf samples. This process includes cutting off the leaf, sectioning it, placing the sample in a frozen SFE vessel, and conducting SFE/SFC analysis. Consequently, the average throughput is approximately 5-7 minutes per sample.

Planning for and Assessing Rigor in Rapid Qualitative Analysis (PARRQA): a consensus-based framework for designing, conducting, and reporting

BackgroundThe use of rapid qualitative methods has increased substantially over the past decade in quality improvement and health services research. These methods have gained traction in implementation research and practice, wherein real-time adjustments are often made to optimize processes and outcomes. This brisk increase begs the questions: what does rigor entail in projects that use rapid qualitative analysis (RQA)? How do we define a pragmatic framework to help research teams design and conduct rigorous and valid rapid qualitative projects? How can authors articulate rigor in their methods descriptions? Lastly, how can reviewers evaluate the rigor of rapid qualitative projects?.MethodsA team of seven interdisciplinary qualitative methods experts developed a framework for ensuring rigor and validity in RQA and methods suitable for this analytic approach. We conducted a qualitative evidence synthesis to identify gaps in the literature and then drew upon literature, standard procedures within our teams, and a repository of rapid qualitative training materials to create a planning and reporting framework. We iteratively refined this framework through 11 group working meetings (60-90 minutes each) over the course of one year and invited feedback on items to ensure their completeness, clarity, and comprehensibility.ResultsThe Planning for and Assessing Rigor in Rapid Qualitative Analysis (PARRQA) framework is organized progressively across phases from design to dissemination, as follows: 1) rigorous design (rationale and staffing), 2) semi-structured data collection (pilot and planning), 3) RQA: summary template development (accuracy and calibration), 4) RQA: matrix analysis (matrices), and 5) rapid qualitative data synthesis. Eighteen recommendations across these sections specify best practices for rigor and validity.ConclusionsRapid qualitative methods play a central role in implementation evaluations, with the potential to yield prompt information and insights about context, processes, and relationships. However, guidance on how to assess rigor is nascent. The PARRQA framework enhances the literature by offering criteria to ensure appropriate planning for and assessment of rigor in projects that involve RQA. This framework provides a consensus-based resource to support high-level qualitative methodological rigor in implementation science.

Implementation lessons learned from the University of California’s Diabetes Prevention Program Initiative

BackgroundThe University of California’s Diabetes Prevention Program (UC DPP) Initiative was implemented systemwide to address diabetes and obesity risk on all 10 campuses. As little is known about implementing lifestyle change programs in university settings, we examined implementation partners’ (i.e., UC DPP leaders and campus leads) perceptions of factors influencing program success on UC campuses.MethodsWe conducted qualitative interviews with UC DPP leaders and campus leads to examine challenges and opportunities with university-based DPP delivery models. Interviews were recorded, professionally transcribed, and reviewed in detail by the research team. Transcripts were analyzed using rapid qualitative analysis (RQA). The study was approved by the UCLA Institutional Review Board. All implementation partners provided verbal informed consent.ResultsTwenty-six implementation partners (8 UC DPP leaders and 18 campus leads) completed interviews in 2021. Seven themes were identified as critical for implementation, including (1) marketing and recruitment (i.e., market and recruit broadly through established channels as well as target at-risk populations); (2) enrollment (i.e., offer the program during convenient times and let participants know what to expect); (3) use an adaptable, evidence-based program; (4) secure funding for the program, participants, lifestyle coaches, and space; (5) hire experienced and dedicated staff and lifestyle coaches; (6) ensure leadership support; and (7) utilize campus linkages and resources. Perceptions of challenges faced with respect to these themes are also described.ConclusionsThis is one of the first studies to examine the challenges and opportunities of delivering an intensive lifestyle change program across 10 university sites. Understanding factors that enhance success of university-based diabetes prevention programs can facilitate UC DPP efforts and help inform delivery strategies of health and wellness programs across other university settings more broadly.

Intervention for the Management of Neuropsychiatric Symptoms to Reduce Caregiver Stress: Protocol for the Mindful and Self-Compassion Care Intervention for Caregivers of Persons Living With Dementia.

Stress related to Alzheimer disease and related dementias (ADRD) is common, particularly among those who care for persons with challenging behaviors and personality or mood changes. Mindfulness and self-compassion programs are efficacious for managing stress. The skills of mindfulness and self-compassion, however, must be integrated with behavioral management skills in order to effectively improve caregiver stress. In this study, we aimed to describe the development of the Mindful and Self-Compassionate Care (MASC) program, the first program that combines mindfulness and self-compassion with behavioral management skills to decrease caregiver stress, and its evaluation in the Supporting Our Caregivers in ADRD Learning (SOCIAL) study. Using the National Institutes of Health (NIH) stage model, we describe 3 phases of work encompassing NIH Stages 1A and 1B. In phase 1, we conducted 5 focus groups (N=28) of stressed individuals caring for persons with ADRD and challenging behaviors. Rapid data analysis informed the development of a 6-week online intervention. Phase 2 (NIH stage 1A) includes an open pilot (N>10) with optional exit interviews. Phase 3 (NIH stage 1B) is a feasibility randomized controlled trial of the intervention versus the Health Education Program control. Primary outcomes focus on feasibility with secondary outcomes encompassing acceptability, credibility, fidelity, and signals of preliminary efficacy. Phase 1 follows traditional recommendations for qualitative analyses (at the point of thematic saturation) which was achieved after 5 focus groups (N=28). For the phase 2 open pilot, up to 12 participants will be recruited. For the phase 3 feasibility study, recruitment of 80 caregivers will allow the assessment of feasibility benchmarks. Data for phase 1 included 5 focus groups. In phases 2 and 3, data collection will occur through REDCap (Research Electronic Data Capture; Vanderbilt University) surveys and an optional qualitative exit interview. Analyses will include hybrid inductive-deductive analyses for qualitative data and assessment of changes in our intervention targets and outcomes using t tests and correlation analyses. In phase 1, caregivers reported interest in a brief, online stress management program. Participants held misconceptions about mindfulness and self-compassion, but after detailed explanation thoughts, these skills could be helpful when directly linked to implementation during caregiving routines. Phases 2 and 3 will be completed by the end of 2025. We describe the protocol for the Supporting Our Caregivers in ADRD Learning study, as well as the development and feasibility testing of the Mindful and Self-Compassionate Care intervention. Future work will include a fully powered efficacy-effectiveness randomized controlled trial. ClinicalTrials NCT05847153; https://clinicaltrials.gov/study/NCT05847153; and ClinicalTrials.gov NCT06276023; https://clinicaltrials.gov/study/NCT06276023. DERR1-10.2196/58356.

On the thermal performance of a three-dimensional cross-ternary hybrid nanofluid over a wedge using a Bayesian regularization neural network approach

Abstract Significance Studying the flow of ternary nanofluids [Ag, Cu, MoS2] holds significant importance in both science and engineering. Ternary nanofluids are vital in advancing thermal management systems, heat exchangers, aerospace, and materials processing applications. Purpose This study investigates the ternary hybrid Carreau nanofluid numerically for thermal proficiency in the inclined magnetized environment. In this study, three distinct nanoparticles of [Ag, Cu, MoS2] and base fluid water over the wedge are used. The velocity of nanofluids is judged under the influence of an inclined magnetic field, and the thermal performance is scrutinized by incorporating the thermal radiation effect. Methodology The physical problem generates partial differential equations, which are transformed into ordinary differential equations (ODEs) through similarity variables. These ODEs are linearized into a system of ODEs and then passed under the bvp4c Matlab program to get the solution. This solution is again trained by an artificial neural network, and further results are obtained with both schemes and compared. Findings The most rapid heat transport analysis is found for ternary hybrid nanofluids compared to bi-hybrid nanofluids. The thermal radiation parameters and the magnetic environment augment the rate of heat transport.

A real-world battery state of charge prediction method based on a lightweight mixer architecture

This paper proposes a lightweight model based on the improved Mixer architecture—TrendFlow-Mixer, designed for efficient prediction of the state of charge (SOC) of lithium-ion batteries. SOC accuracy is crucial for the safe operation, usage, and maintenance of power batteries. When predicting the SOC of real-world batteries, the algorithm's generalization, robustness, and computational efficiency across different battery types must be considered. To this end, TrendFlow-Mixer introduces targeted improvements to battery characteristics, significantly alleviating the Mixer architecture's lag in SOC prediction. The model's core structure integrates the Fast Fourier Transform and arrangement transformation convolutional structures. This allows for efficient extraction of domain-specific spatial features and enables cross-channel correlation interactions. This reduces the model's parameter size while enhancing its adaptability to new domains. The model quickly captures data trend changes and identifies data flow patterns, improving computational efficiency by 70 % compared to Transformer-based architectures. TrendFlow-Mixer employs a dual-prediction head, combining rapid feature change analysis with a nonlinear prediction output to generate SOC predictions. This design facilitates fine-tuning of the model's structure and parameters while enhancing overall flexibility. Experimental results show that in a laboratory dynamic condition dataset, the model achieves a minimum mean absolute error (MAE) of 0.11 %, with the maximum prediction error under extreme temperatures controlled within 4.66 %. In validation using real-world data from the BMW i3, the mean absolute percentage error remains around 1 %, with a minimum MAE of 0.465 %. The results demonstrate that TrendFlow-Mixer possesses high computational efficiency, excellent generalization, and adaptability, effectively predicting SOC trends.

Rapid analysis method of helicopter fuselage bullet damage based on bullet penetration theory

Helicopter fuselage has a complex shape and structure. Numerical simulation analysis of helicopter fuselage bullet damage based on the finite element method is computationally intensive and takes a long time. It is difficult to quickly analyze and evaluate helicopter fuselage bullet damage in a complex multi-bomb environment. To this end, the bullet penetration theory and the bullet impact calculation model were coupled to establish a rapid analysis method for helicopter fuselage bullet damage, and the accuracy of the rapid analysis method was verified by comparing with test results and finite element numerical simulation results. Then the helicopter fuselage bomb coordinates, residual speed and fuselage damage area of the multi-bomb strike were studied. Research shows that the residual velocity calculated by the rapid analysis method is consistent with the test results; compared with the finite element numerical simulation of bullet damage, the maximum error of the projectile residual velocity calculated by the rapid analysis method is 4.7%, and the maximum error of the fuselage damage area is 9.56%, and the calculation time is reduced 92.1%. Computational efficiency is significantly improved. As the incident angle of the projectile increases, the residual velocity decreases and the damage area increases; when the incident angle of the projectile is too large, the projectile cannot penetrate the surface of the fuselage, causing sink-like sliding damage, causing a larger area of damage.

ANALISIS KEBERLANJUTAN PERKEBUNAN KELAPA SAWIT SWADAYA DI KECAMATAN SEKERNAN KABUPATEN MUARO JAMBI

This study aims to determine the general description of smallholder palm oil in Sekernan District, Muaro Jambi Regency, and analyze their sustainability status and sensitive attributes that affect them. The method used in this study is descriptive qualitative and quantitative analysis to determine the status of the sustainability index of smallholder palm oil using MDS Rapid Apparasial analysis, which is modified to RAP-PalmOil, and leverage analysis is used to determine its sensitive attributes. This research was conducted in Suko Awin Jaya, Bukit Baling, Tantan, and Tunas Mudo villages. The sampling location in this study was determined purposively (purposive) with a sample of 100 respondents, who were then determined by simple random sampling. The study results show that the sustainability status of smallholder palm oil in Sekernan District is multidimensionally considered quite sustainable. The economic dimension provides the highest sustainability index and is followed by the social and ecological dimension, which is considered quite sustainable. The sensitive attribute or attribute that has the most influence on the sustainability index value on the ecological dimension is the fertilization period, while the economic dimension is farming income, and the sensitive attribute on the social dimension is conflict resolution

Telemedicine-Related Opioid Use Disorder Services in Underserved Populations: A Qualitative Evaluation of the Waiver Era.

Introduction: This pilot study examined access to telemedicine-related opioid use disorder (OUD) treatment in underserved communities in Houston, Texas before July 30, 2023. Methods: Participants, both patients and providers, were recruited in partnership with local substance use treatment clinics. Both groups shared experiences before and after the waiver era. Rapid qualitative analysis was conducted by the research team. Results: Fourteen qualitative interviews were conducted via Zoom with 5 treatment providers and 9 self-identified Black or Hispanic patient participants. Participants generally approved telemedicine for OUD treatment due to its technological accessibility and flexibility. However, concerns about technology, care quality, relationship building, and privacy were common among both patients and providers. Discussion: Our study highlights the underutilization of telemedicine for OUD treatment in underserved Houston communities. Efforts to address current limitations and leverage recent policy changes can help bridge the utilization gap in underserved areas.

Paper Spray Ionization Mass Spectrometry Coupled with Paper-Based Three-Dimensional Tumor Model for Rapid Metabolic Gradient Profiling.

The tumor microenvironment (TME), especially with its complicated metabolic characteristics, will dynamically affect the proliferation, migration, and drug response of tumor cells. Rapid metabolic analysis brings out a deeper understanding of the TME, while the susceptibility and environmental dependence of metabolites extremely hinder real-time metabolic profiling since the TME is easily disrupted. Here, we directly integrated paper spray ionization mass spectrometry with a paper-based three-dimensional (3D) tumor model, realizing the rapid capture of metabolic gradients. The entire procedure, from sample preparation to mass spectrometry detection, took less than 4 min, which was able to provide metabolic results close to real time and contributed to understanding the real metabolic processes. At present, our method successfully detected 160 metabolites; notably, over 40 significantly gradient metabolites were revealed across the six layers of the paper-based 3D tumor model. At least 22 gradient metabolites were reported to be associated with cell viability. This strategy was powerful enough to rapidly profile metabolic gradients of a paper-based 3D tumor model for revealing cell viability changes from a metabolomics perspective.

Strengthen farmers' fertilizer self-sufficiency in the tourist area with a reduce-reuse-recycle system

Mining activities at Tebing Breksi are a vital source of income for the community in Sambirejo village, Sleman Regency, Yogyakarta. However, these activities have caused several environmental issues, such as lowered pH levels, reduced soil fertility, shallow soil profiles, and water scarcity. Additionally, mining has led to biodiversity loss and land degradation, making post-mining reclamation essential to restore the area's environmental and social functions. This project aimed to enhance the community’s skills in soil analysis and the production of liquid organic fertilizer using effective methods. The activities took place in the Tebing Breksi tourist area, located in Nglengkong, Sambirejo Village, Prambanan District, Sleman Regency. The project included a Focus Group Discussion (FGD), soil fertility analysis using test kits, and the creation of liquid organic fertilizer from household waste. During the FGD, it was discovered that the local community had not received prior training on producing fertilizer from household and organic waste. Until then, they relied on expensive commercial fertilizers to quickly stabilize soil pH. Through this training, the community learned how to perform rapid soil chemical analysis and produce liquid organic fertilizer independently. As a result, they can now apply the fertilizer locally, improving soil conditions and reducing their reliance on costly commercial products.

Conjugation of visual enhancers in lateral flow immunoassay for rapid forensic analysis: A critical review.

During crime scene investigations, numerous traces are secured and may be used as evidence for the evaluation of source and/or activity level propositions. The rapid chemical analysis of a biological trace enables the identification of body fluids and can provide significant donor profiling information, including age, sex, drug abuse, and lifestyle. Such information can be used to provide new leads, exclude from, or restrict the list of possible suspects during the investigative phase. This paper reviews the state-of-the-art labelling techniques to identify the most suitable visual enhancer to be implemented in a lateral flow immunoassay setup for the purpose of trace identification and/or donor profiling. Upon comparison, and with reference to the strengths and limitations of each label, the simplistic one-step analysis of noncompetitive lateral flow immunoassay (LFA) together with the implementation of carbon nanoparticles (CNPs) as visual enhancers is proposed for a sensitive, accurate, and reproducible in situ trace analysis. This approach is versatile and stable over different environmental conditions and external stimuli. The findings of the present comparative analysis may have important implications for future forensic practice. The selection of an appropriate enhancer is crucial for a well-designed LFA that can be implemented at the crime scene for a time- and cost-efficient investigation.

Fast On-Site Speciation and High Spatial Resolution Imaging of Labile Arsenic in Freshwater and Sediment Using the DGT-SERS Sensor.

Diffusive gradients in thin films (DGT) technique is renowned for in situ passive sampling but not for rapid on-site analysis, whereas surface-enhanced Raman spectroscopy (SERS) excels in ultrasensitive on-site detection but is limited by substrate contamination from complex matrices. Here, a hierarchical nanostructure of silver (Ag) mirror-supported large Ag nanoparticles (∼120 nm) was grown in situ in polyacrylamide hydrogel with a restricted pore size (PAM/Ag mirror/AgNPs) to serve as both the DGT binding phase and the SERS substrate. The substrate exhibited a maximum electric field enhancement factor of 9.9 × 108 and a signal relative standard error of 4.8%. Using the DGT-SERS sensor, As(III) and As(V) in freshwater were simultaneously detected at limits of 0.9 and 0.8 μg L-1, respectively, applicable across a wide range of environmental conditions. The DGT-SERS effectively mitigated the interfacial reduction of As(V) caused by humic acid by excluding it from plasmonic hotspots through size exclusion of the diffusive layer. The Raman analysis of a DGT sample in the field requires only 2 s using a portable spectrometer without DGT device disassembly. More importantly, the DGT-SERS captured the first two-dimensional image of As(III) and As(V) in one DGT at the micron scale resolution, revealing their spatially supplementary distribution patterns at the sediment-water interface. This study paves the way for next-generation speciation imaging DGT and the application of SERS in complex environments.

Classification of green tea and quantitative prediction of catechin content based on indicator displacement array sensors

Green tea, renowned for its health benefits and cultural significance, exhibits variations in quality and biochemical composition across different grades and varieties. Traditional quality assessment methods are often labour-intensive and time-consuming, rendering them impractical for rapid analysis. This study employed a sensitive indicator displacement array sensor along with supervised learning algorithms—Extreme learning machine, Support vector machine, Random forest, and Back propagation neural network—to accurately classify green tea varieties and grades, as well as predict catechin content. Sensor components were initially screened based on their responsiveness to epigallocatechin gallate, and subsequently developed using specific indicator-receptor combinations: Pyrocatechol Violet/Fe2+, Alizarin Red S/Phenylboronic Acid, and Bromopyrogallol Red/3-Nitrophenylboronic Acid. Validation was performed through monitoring changes in RGB values and UV–visible spectroscopy. Discrimination among Anji white tea grades was assessed using principal component analysis and further refined through supervised algorithms, achieving high accuracy rates, especially with Random forest and Extreme learning machine models, which excelled in both calibration and prediction sets. These methodologies were extended to classify ten green tea varieties, demonstrating robustness and high accuracy in a multiclass setting. Independent validation confirmed the models’ efficacy in-class differentiation, with most achieving an accuracy of over 90%. Moreover, the quantitative prediction of catechin content was enhanced using nonlinear models, specifically Support vector regression optimized with the Particle swarm optimization algorithm. These models exhibited superior performance compared to linear models, particularly in effectively predicting individual catechin levels. These findings highlight the potential of combining sensor technologies with supervised learning algorithms to enhance tea quality assessment and categorization, establishing a robust framework for future applications in tea quality control.

AMIR: a multi-omics data platform for Asteraceae plants genetics and breeding research.

As the largest family of dicotyledon, the Asteraceae family comprises a variety of economically important crops, ornamental plants and numerous medicinal herbs. Advancements in genomics and transcriptomic have revolutionized research in Asteraceae species, generating extensive omics data that necessitate an efficient platform for data integration and analysis. However, existing databases face challenges in mining genes with specific functions and supporting cross-species studies. To address these gaps, we introduce the Asteraceae Multi-omics Information Resource (AMIR; https://yanglab.hzau.edu.cn/AMIR/), a multi-omics hub for the Asteraceae plant community. AMIR integrates diverse omics data from 74 species, encompassing 132 genomes, 4 408 432 genes annotated across seven different perspectives, 3897 transcriptome sequencing samples spanning 131 organs, tissues and stimuli, 42 765 290 unique variants and 15 662 metabolites genes. Leveraging these data, AMIR establishes the first pan-genome, comparative genomics and transcriptome system for the Asteraceae family. Furthermore, AMIR offers user-friendly tools designed to facilitate extensive customized bioinformatics analyses. Two case studies demonstrate AMIR's capability to provide rapid, reproducible and reliable analysis results. In summary, by integrating multi-omics data of Asteraceae species and developing powerful analytical tools, AMIR significantly advances functional genomics research and contributes to breeding practices of Asteraceae.

Tungsten Concentrations in Mittersill Scheelite Ore Quantified with X-Ray Computed Tomography

AbstractDrill cores from the Mittersill tungsten mine, also called Felbertal, were scanned with a drill core scanner using X-ray computed tomography (XCT) and X-ray fluorescence (XRF). Matrix amphiboles and quartz stand out as peaks in the XCT attenuation histogram, whereas the main ore mineral scheelite has distinctly high attenuation. Volumetric concentrations of the XCT high attenuation phase show an excellent correlation with tungsten lab assay values for 56 samples. One outlier, which can be attributed to a high bismuth concentration, as observed with the drill core scanner, needs a correction term to fall close to the linear relation formed by all other samples. The results suggest the technique could be used for rapid tungsten analysis in this, or similar, geological settings. It is evident that scheelite shows up as high attenuation values in the XCT data, which enables further ore characterization studies in 3D.

Ergot alkaloid control in biotechnological processes and pharmaceuticals (a mini review).

The control of ergot alkaloids in biotechnological processes is important in the context of obtaining new strain producers and studying the mechanisms of the biosynthesis, accumulation and secretion of alkaloids and the manufacturing of alkaloids. In pharmaceuticals, it is important to analyze the purity of raw materials, especially those capable of racemization, quality control of dosage forms and bulk drugs, stability during storage, etc. This review describes the methods used for qualitative and quantitative chemical analysis of ergot alkaloids in tablets and pharmaceutic forms, liquid cultural media and mycelia from submerged cultures of ergot and other organisms producing ergoalkaloid, sclerotias of industrial Claviceps spp. parasitic strains. We reviewed analytical approaches for the determination of ergopeptines (including their dihydro- and bromine derivatives) and semisynthetic ergot-derived medicines such as cabergoline, necergoline and pergolide, including precursors for their synthesis. Over the last few decades, strategies and approaches for the analysis of ergoalkaloids for medical use have changed, but the general principles and objectives have remained the same as before. These changes are related to the development of new genetically improved strains producing ergoalkaloids and the development of technologies for the online control of biotechnological processes and pharmaceutical manufacturing ("process analytical technologies," PAT). Overall, the industry is moving toward "smart manufacturing." The development of approaches to production cost estimation and product quality management, manufacturing management, increasing profitability and reducing the negative impact on personnel and the environment are integral components of sustainable development. Analytical approaches for the analysis of ergot alkaloids in pharmaceutical raw materials should have high enough specificity for the separation of dihydro derivatives, enantiomers and R-S epimers of alkaloids, but low values of the quantitative detection limit are less frequently needed. In terms of methodology, detection methods based on mass spectrometry have become more developed and widespread, but NMR analysis remains in demand because of its high accuracy and specificity. Both rapid methods and liquid chromatography remain in demand in routine practice, with rapid analysis evolving toward higher accuracy owing to improved analytical performance and new equipment. New composite electrochemical sensors (including disposable sensors) have demonstrated potential for real-time process control.

Plasmonic Coacervate as a Droplet-Based SERS Platform for Rapid Enrichment and Microanalysis of Hydrophobic Payloads.

A novel and simple coacervate microdroplet-based detection platform for the quantification of trace hydrophobic analytes is presented. Herein, taking advantage of the effective encapsulation and enrichment performance of the condensed coacervates, plasmonic metallic silver nanoparticles (AgNPs) and target hydrophobic analytes are simultaneously concentrated into a single microdroplet. The coencapsulation of AgNPs within coacervates promotes the formation of aggregates with a lot of "hot spots" for surface-enhanced Raman scattering (SERS) enhancement, facilitating the sensitive analysis of hydrophobic analytes by SERS technology. Such plasmonic coacervates are easily prepared and exhibit good reproducibility and signal uniformity. Optimized SERS performance by modulating the volume of encapsulated AgNPs enables quantitative determination of hydrophobic analytes of Nile Red, chlorpyrifos, benzo[e]pyrene, 20 and 50 nm polystyrene nanoplastics with low detection limits of 10-12 M, 10-9 M, 10-10 M, 0.05 ppb, and 0.5 ppb, and an approximately linear correlation between SERS signals and the analytical concentrations. This study opens a new convenient SERS platform for the ultrasensitive detection of hydrophobic hazardous substances, potentially becoming a rapid analysis method for extensive applications ranging from food safety to environment monitoring.

Timecourse of bottom-up and top-down language processing during a picture-based semantic priming task

ABSTRACT Understanding spoken language requires rapid analysis of incoming information at multiple levels. Information at lower levels (e.g. acoustic/phonetic) cascades forward to affect processing at higher levels (e.g. lexical/semantic), and higher-level information may feed back to influence lower-level processing. Most studies have sought to examine a single stage of processing in isolation. Consequently, there is a poor understanding of how different stages relate temporally. In the present study, we characterise multiple stages of linguistic processing simultaneously as they unfold. Listeners (N = 30) completed a priming task while we collected their EEG, where a picture (e.g. of a peach) biased them to expect a target word from a minimal pair (e.g. beach/peach). We examine the processes of perceptual gradiency, semantic integration, and top-down feedback, to yield a more complete understanding of how these processes relate in time. Then, we discuss how the results from simplified priming paradigms may compare to more naturalistic settings.

Space-Confined Amplification for In Situ Imaging of Single Nucleic Acid and Single Pathogen on Biological Samples.

Direct in situ imaging of nucleic acids on biological samples is advantageous for rapid analysis without DNA extraction. However, traditional nucleic acid amplification in aqueous solutions tends to lose spatial information because of the high mobility of molecules. Similar to a cellular matrix, hydrogels with biomimetic 3D nanoconfined spaces can limit the free diffusion of nucleic acids, thereby allowing for ultrafast in situ enzymatic reactions. In this study, hydrogel-based in situ space-confined interfacial amplification (iSCIA) is developed for direct imaging of single nucleic acid and single pathogen on biological samples without formaldehyde fixation. With a polyethylene glycol hydrogel coating, nucleic acids on the sample are nanoconfined with restricted movement, while in situ amplification can be successfully performed. As a result, the nucleic acids are lighted-up on the large-scale surface in 20min, with a detection limit as low as 1 copy/10 cm2. Multiplex imaging with a deep learning model is also established to automatically analyze multiple targets. Furthermore, the iSCIA imaging of pathogens on plant leaves and food is successfully used to monitor plant health and food safety. The proposed technique, a rapid and flexible system for in situ imaging, has great potential for food, environmental, and clinical applications.