Prolonged Processing Time Research Articles

Surface modification of fabrics using dielectric barrier discharge plasma for improved antifouling performance

Abstract Surface modification of fabrics is an effective way to endow them with antifouling properties while still maintain their key advantages like comfort, softness, and stretchability. Herein, an atmospheric pressure dielectric barrier discharge (DBD) plasma method is demonstrated for the processing of silk fabrics using 1H,1H,2H,2H-perfluorodecyltriethoxysilane (PFDS) as the precursor. The results showed the successful grafting of PFDS groups on the surface of silk fabrics without causing damage. Meanwhile, the gas temperature is rather low during the whole processing procedure, suggesting the non-equilibrium characteristics of DBD plasma. The influence of processing parameters on fabrics was systematically investigated, like the PFDS concentration, plasma treatment time, and plasma discharge power. An optimum processing condition was determined to be PFDS concentration: 8 wt%, plasma processing time: 40 s, and plasma power: 11.87 W. However, with the prolonged plasma processing time or enhanced plasma power, the plasma-grafted PFDS films could be degraded. Further study revealed that plasma processing of silk fabrics with PFDS would lead to the change of their chemical composition and surface roughness. As a result, the surface energy of the fabrics was reduced, accompanied by the improved water and oil repellency properties as well as enhanced antifouling performance. Besides, the plasma-grafted PFDS films also had good durability and stability. By extending the fabrics to polyester and wool against different oil-/water-based stains, the DBD plasma surface modification technique demonstrated good versatility in improving the antifouling properties of fabrics. This work provides a guidance for the surface modification of fabrics using DBD plasma to confer them with desirable functionalities.

Oxidative Passivation of FAPbI3 via Femtosecond Laser for Enhanced Photoluminescence and Photoresponse Performance

AbstractThe presence of uncoordinated Pb atoms in perovskite materials, acting as non‐radiative recombination centers, significantly degrades the performance of perovskite‐based photoelectronic devices. Addressing this issue by passivating these deep energy level defects is essential for enhancing device performance. By now, the passivation methods are largely proposed by chemical additive engineering methods, which involve the oxidation of Pb through chemical additives. However, chemical passivation methods are often plagued by issues such as prolonged processing times, uncontrollable passivation regions uncontrollable, and environmental contamination. Here, a novel femtosecond laser oxidative passivation (FsLOP) technique for FAPbI3 is proposed, evidenced by X‐ray photoelectron spectroscopy (XPS) revealing the formation of Pb─O bonds. The role of peroxide anions in oxidation passivation is highlighted as pivotal. The oxidative passivation effect is further corroborated by a four‐fold increase in photoluminescence (PL) intensity and an exceptional improvement in photoresponse performance, achieving a detectivity of 6.77 × 1012 Jones. This work provides a deeper insight into the interaction between femtosecond lasers and perovskite materials, proposing an efficient, precisely controlled, and environmentally friendly way to improve the performance of perovskite photoelectronic devices.

Isotropic Brain MRI Reconstruction from Orthogonal Scans Using 3D Convolutional Neural Network.

As an alternative to true isotropic 3D imaging, image super-resolution (SR) has been applied to reconstruct an isotropic 3D volume from multiple anisotropic scans. However, traditional SR methods struggle with inadequate performance, prolonged processing times, and the necessity for manual feature extraction. Motivated by the exceptional representational ability and automatic feature extraction of convolutional neural networks (CNNs), in this work, we present an end-to-end isotropic MRI reconstruction strategy based on deep learning. The proposed method is based on 3D convolutional neural networks (3D CNNs), which can effectively capture the 3D structural features of MRI volumes and accurately predict potential structure. In addition, the proposed method takes multiple orthogonal scans as input and thus enables the model to use more complementary information from different dimensions for precise inference. Experimental results show that the proposed algorithm achieves promising performance in terms of both quantitative and qualitative assessments. In addition, it can process a 3D volume with a size of 256 × 256 × 256 in less than 1 min with the support of an NVIDIA GeForce GTX 1080Ti GPU, which suggests that it is not only a quantitatively superior method but also a practical one.

Innovative tools and protocols for the regeneration of urban and peri-urban districts towards climate neutrality

Abstract Cities represent the foremost contributor to the escalating growth in energy consumption, constituting a substantial portion of global greenhouse gas (GHG) emissions. In the face of climate change and the heightened frequency and severity of extreme events, urgent policies and actions are imperative for cities to enhance resilience and instill a sense of responsibility among their inhabitants. Additionally, establishing monitoring and reporting models to track progress in implementing sustainable urban development measures is crucial. The calculation of CO2 emissions in cities demands a multidimensional approach, encompassing sectors such as energy, buildings, water, green spaces, transport, waste, and others. Models and diagnostic tools are available for the different fields of interest. These are valuable elements; however, they often necessitate specialized expertise and prolonged processing times for accurate result interpretation, sometimes conflicting with the need for prompt, reliable guidance. This contribution aims to define innovative tools and protocols, along with a specific performance verification model (easy model), concerning mitigation and adaptation to climate change. These tools are designed to be easy to apply and usable from the preliminary project stages for the redevelopment/regeneration of existing architecture and urban districts.

Validation of the Virtual Reality Stroop Room: Effects of inhibiting interfering information under time-pressure and task-switching demands

The physiological stress response affects executive functions, such as inhibition, as assessed by the Stroop Color and Word Test. In this study, we investigated the effects of the Virtual Reality Stroop Room (VRSR), a research paradigm assessing these cognitive top-down processes while inducing mild acute stress, on self-reported stress states, heart rate, salivary alpha-amylase, and cortisol. Our sample consisted of 89 participants (52 women; Age: 23.60 ± 3.88 years) and was evenly allocated to the three conditions of the VRSR (regular, time pressure, and rotation). The Stroop Effect, reflected in prolonged processing times and increased errors in the incongruent phase, was observed. Participants reported heightened Distress and Engagement post-experiment, alongside lower Worry, assessed via the Short Stress State Questionnaire. Scores from the Positive and Negative Affect Schedule indicated elevated positive affect and decreased negative affect post-study. With regard to biosignals we found that heart rate was higher in the incongruent phase, compared to the congruent phase and a significant time × condition interaction was observed. Salivary alpha-amylase exhibited a significant time effect. Results for cortisol do not support a uniform response of the hypothalamus-pituitary-adrenal (HPA) axis. In conclusion, the VRSR appears to be a valid measure for executive functions while activating the sympathetic nervous system, but not the HPA axis. Its current implementation induces mild physiological and psychological stress responses, with fewer adverse reactions compared to the Trier Social Stress Test. Future studies should leverage the adaptability of virtual reality applications to refine this research paradigm.

The first magnetic resonance imaging compatible 3D printer

Background: The current study presents the development of a magnetic resonance imaging (MRI)-compatible silicone-based 3D printer capable of producing patient-specific implants within MRI scanners. Methods: The printing device incorporates 3 piezoelectrically-actuated linear motion stages assigned for navigating a custom-made silicone extruder to develop the desired 3D model based on preoperative MRI scans of the damaged anatomy. The structural components were manufactured on a rapid prototyping machine with thermoplastic and compactly assembled utilizing non-magnetic materials to ensure fit and safe functioning of the system within the MRI bore. Results: The printing system was successfully integrated with a high-field MRI scanner and operated safely while maintaining sufficient imaging quality. The robotic motion mechanism exhibited excellent repeatability and achieved submillimeter accuracy, demonstrating its capability for precise positioning of the extruder. Conclusion: The proposed 3D printer may hold promise as valuable tool for personalized tissue reconstruction by real-time printing with biocompatible silicone on the MRI table. However, challenges such as prolonged processing times and related high costs will possibly hinder its adoption in clinical practice.

51755 Factors associated with prolonged first stage tumor processing time in the Mohs laboratory

51755 Factors associated with prolonged first stage tumor processing time in the Mohs laboratory

A novel and efficient method for synthesizing reduced charge montmorillonite

Layer charge (LC), a paramount physiochemical characteristic of montmorillonite (Mt), has been garnered considerable attention on its regulating and impact on modified Mt products. Incorporating small cations such as Li+ into the tetrahedral/octahedral sheets of Mt, or employing acid treatment, are feasible methods to reduce its LC. However, conventional techniques for producing reduced charge Mt (RC-Mt) often suffer from prolonged processing times, complex procedures, and high consumption of water and energy. Moreover, the formation mechanisms of RC-Mt remain inadequately understood. To address these challenges, this study proposes a novel and efficient method combining dry lithiation and microwave irradiation to prepare RC-Mt, and compares it with a conventional acid treatment. The research also explores the applications of RC-Mt, focusing on organic modification and the evaluation of adsorption performance. It was found that the cation exchange capacity (CEC) of RC-Mt diminished with increasing microwave power and time, LiCl dosage, HCl concentration, acidification duration and temperature. It has been demonstrated that the dry lithiation & microwave method significantly reduces the total preparation time of RC-Mt to 12 min while eliminating the generation of wastewater. The pivotal advantages of this approach include its efficiency, time-saving, and minimal environmental impact. Analytical techniques such as X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and solid-state nuclear magnetic resonance spectroscopy (SSNMR) revealed that microwave irradiation facilitates the migration of Li+ into the hexagonal cavities and octahedral vacancies of the Mt tetrahedral sheets. Additionally, acid treatment results in the dissolution of octahedral cations from Mt. The modified products of RC-Mt with lower CEC, exhibited enhanced adsorption of perchlorate ions (ClO4−). These findings offer significant insights into the modification of Mt and its potential for advanced technological applications, presenting a promising avenue for future research.

A glassy carbon electrode modified with gold decorated iron oxide/ carbon dots for light assisted voltammetric detection of antibiotic resistant microbe Enterococcusfaecalis

Detecting bacteria is essential in managing significant health concerns as it enables timely intervention, reducing complications and improving patient outcomes, particularly in treating common infections that necessitate precise identification for effective symptom management. Enterococcus species represent a notable threat in hospital-acquired infections and urinary tract infections (UTIs), given the increasing prevalence of strains resistant to multiple antibiotics, unresponsive to standard therapies, and carrying various virulence factors. Traditional approaches to identifying Enterococcus faecalis (E. faecalis) have limitations, including prolonged processing times, limited sensitivity, and the potential for false positive results. While Polymerase Chain Reaction (PCR) is a valuable tool, it is susceptible to contamination and variations in DNA concentration. The emerging technique of Photoelectrochemical (PEC) holds promise for enhancing E. faecalis detection by leveraging photogenerated electrons and holes. This study introduces a rapid and precise approach utilizing a light-assisted electrochemical biosensor featuring a glassy carbon electrode modified with a nanocomposite of gold-coated iron oxide and carbon dots (Au@Fe3O4/CDs). The nanocomposite was successfully synthesized and underwent thorough characterization. The investigation has a detection range from 1 to 14 CFU mL−1, along with a notably low limit of detection (LOD: 3 CFU mL−1, LOQ: 10 CFU mL−1). Rigorous examination of real-world samples such as food, water, and soil demonstrated exceptional specificity, reproducibility, and long-term stability of the sensor. The applications of the Au@Fe3O4/CDs nanocomposite in PEC processes underscore the potential of this innovative approach in addressing health concerns associated with bacterial infections and delivering real-time impacts for both healthcare and environmental domains.

Improving Muscat Hamburg Wine Quality with Innovative Fermentation Strategies Using Schizosaccharomyces pombe Derived from Fermented Grains of Sauce-Flavor Baijiu.

This study investigates innovative approaches to improve the quality and aroma characteristics of Muscat Hamburg wine production by substituting the conventional Saccharomyces cerevisiae yeast with an efficient fermentation strain of Schizosaccharomyces pombe. The typical use of S. cerevisiae in Muscat Hamburg wine often leads to uniformity and prolonged processing times, requiring subsequent malolactic fermentation to degrade excessive malic acid. The study advocates for the replacement of S. cerevisiae with a specific S. pombe strain, Sp-410, isolated from the fermented grains of sauce-flavor Baijiu, a Chinese spirit. Muscat Hamburg wine fermented with the S. pombe strain demonstrates decreased malic acid levels, offering a potential alternative to malolactic fermentation. However, exclusive S. pombe fermentation may result in an overproduction of acetic acid metabolites, leading to a monotonous taste. In response, the study proposes a mixed fermentation approach, combining the S. pombe strain with a Saccharomyces uvarum strain and a non-Saccharomyces yeast, Torulaspora delbrueckii. The optimized mixed fermentation strategies (M:SP+TD and M60SP+TD) involve specific proportions and intervals of inoculation, aiming to enhance the quality and aroma complexity of Muscat Hamburg wine. In conclusion, this research contributes to advancing the production of high-quality Muscat Hamburg wines, utilizing S. pombe as the primary yeast strain and implementing mixed fermentation methodologies.

Extraction of Corn Plant Phenotypic Parameters with Keypoint Detection and Stereo Images

Corn is a global crop that requires the breeding of superior varieties. A crucial aspect of the breeding process is the accurate extraction of phenotypic parameters from corn plants. The existing challenges in phenotypic parameter extraction include low precision, excessive manual involvement, prolonged processing time, and equipment complexity. This study addresses these challenges by opting for binocular cameras as the data acquisition equipment. The proposed stereo corn phenotype extraction algorithm (SCPE) leverages binocular images for phenotypic parameter extraction. The SCPE consists of two modules: the YOLOv7-SlimPose model and the phenotypic parameter extraction module. The YOLOv7-SlimPose model was developed by optimizing the neck component, refining the loss function, and pruning the model based on YOLOv7-Pose. This model can better detect bounding boxes and keypoints with fewer parameters. The phenotypic parameter extraction module can construct the skeleton of the corn plant and extract phenotypic parameters based on the coordinates of the keypoints detected. The results showed the effectiveness of the approach, with the YOLOv7-SlimPose model achieving a keypoint mean average precision (mAP) of 96.8% with 65.1 million parameters and a speed of 0.09 s/item. The phenotypic parameter extraction module processed one corn plant in approximately 0.2 s, resulting in a total time cost of 0.38 s for the entire SCPE algorithm to construct the skeleton and extract the phenotypic parameters. The SCPE algorithm is economical and effective for extracting phenotypic parameters from corn plants, and the skeleton of corn plants can be constructed to evaluate the growth of corn as a reference. This proposal can also serve as a valuable reference for similar functions in other crops such as sorghum, rice, and wheat.

Formation of Si nanopillars through partial sacrificing in super passivation reactive ion etching

The vertical gate-all-around (VGAA) metal-oxide-semiconductor field-effect transistor (MOSFET) holds remarkable potential in the three-dimensional (3D) integrated circuits (ICs), primarily owing to its capacity for vertical integration. The Si nanopillar, a crucial channel in the VGAA MOSFET, is conventionally shaped via the reactive ion etching (RIE) system employing SF6/O2. Past studies have indicated that high O2 gas conditions in RIE often result in Si grasses irregular nanostructures, such as nanospikes on the bottom surface, due to over-passivation. However, this study revealed that ultrahigh O2 proportions (>70%), especially when combined with low chamber pressure, inhibit the development of Si grasses in the RIE system (termed as super passivation). Nevertheless, this scenario leads to the segmentation of the Si nanopillar. To address this issue, a proposed partial sacrificing method, achieved by sacrificing the upper segment of the nanopillar through prolonged processing time and reduced mask size, successfully yielded Si nanopillars without Si grasses. Furthermore, an empirical model was developed to elucidate how experimental parameters influence etching characteristics, encompassing etching rate and Si nanopillar shape, through a systematic examination of the RIE etching process. This research significantly contributes to the production of VGAA MOSFETs and 3D ICs.

Oil Palm Bunch Ripeness Classification and Plantation Verification Platform: Leveraging Deep Learning and Geospatial Analysis and Visualization

Oil palm cultivation thrives as a prominent agricultural endeavor within the southern region of Thailand, where the country ranks third globally in production, following Malaysia and Indonesia. The assessment of oil palm bunch ripeness serves various purposes, notably in determining purchasing prices, pre-harvest evaluations, and evaluating the impacts of disasters or low market prices. Presently, two predominant methods are employed for this assessment, namely human evaluation, and machine learning for ripeness classification. Human assessment, while boasting high accuracy, necessitates the involvement of farmers or experts, resulting in prolonged processing times, especially when dealing with extensive datasets or dispersed fields. Conversely, machine learning, although capable of accurately classifying harvested oil palm bunches, faces limitations concerning its inability to process images of oil palm bunches on trees and the absence of a platform for on-tree ripeness classification. Considering these challenges, this study introduces the development of a classification platform leveraging machine learning (deep learning) in conjunction with geospatial analysis and visualization to ascertain the ripeness of oil palm bunches while they are still on the tree. The research outcomes demonstrate that oil palm bunch ripeness can be accurately and efficiently classified using a mobile device, achieving an impressive accuracy rate of 99.89% with a training dataset comprising 8779 images and a validation accuracy of 96.12% with 1160 images. Furthermore, the proposed platform facilitates the management and processing of spatial data by comparing coordinates derived from images with oil palm plantation data obtained through crowdsourcing and the analysis of cloud or satellite images of oil palm plantations. This comprehensive platform not only provides a robust model for ripeness assessment but also offers potential applications in government management contexts, particularly in scenarios necessitating real-time information on harvesting status and oil palm plantation conditions.

Research on Online Monitoring Technology and Filtration Process of Inclusions in Aluminum Melt.

Online monitoring and real-time feedback on inclusions in molten metal are essential for metal quality control. However, existing methods for detecting aluminum melt inclusions face challenges, including interference, prolonged processing times, and latency. This paper presents the design and development of an online monitoring system for molten metal inclusions. Initially, the system facilitates real-time adjustment of signal acquisition parameters through a multiplexer. Subsequently, it employs a detection algorithm capable of swiftly extracting pulse peaks, with this task integrated into our proprietary host computer software to ensure timely detection and data visualization. Ultimately, we developed a monitoring device integrated with this online monitoring system, enabling the online monitoring of the aluminum alloy filtration process. Our findings indicate that the system can accurately measure the size and concentration of inclusions during the filtration process in real time, offering enhanced detection speed and stability compared to the industrial LiMCA CM (liquid metal cleanliness analyzer continuous monitoring) standard. Furthermore, our evaluation of the filtration process demonstrates that the effectiveness of filtration significantly improves with the increase in inclusion sizes, and the synergistic effect of combining CFF (ceramic foam filter) and MCF (metallics cartridge filter) filtration methods exceeds the performance of the CFF method alone. This system thus provides valuable technical support for optimizing filtration processes and controlling inclusion quality.

Centrifugo-Pneumatic Reciprocating Flowing Coupled with a Spatial Confinement Strategy for an Ultrafast Multiplexed Immunoassay.

Immunoassays serve as powerful diagnostic tools for early disease screening, process monitoring, and precision treatment. However, the current methods are limited by high costs, prolonged processing times (>2 h), and operational complexities that hinder their widespread application in point-of-care testing. Here, we propose a novel centrifugo-pneumatic reciprocating flowing coupled with spatial confinement strategy, termed PRCM, for ultrafast multiplexed immunoassay of pathogens on a centrifugal microfluidic platform. Each chip consists of four replicated units; each unit allows simultaneous detection of three targets, thereby facilitating high-throughput parallel analysis of multiple targets. The PRCM platform enables sequential execution of critical steps such as solution mixing, reaction, and drainage by coordinating inherent parameters, including motor rotation speed, rotation direction, and acceleration/deceleration. By integrating centrifugal-mediated pneumatic reciprocating flow with spatial confinement strategies, we significantly reduce the duration of immune binding from 30 to 5 min, enabling completion of the entire testing process within 20 min. As proof of concept, we conducted a simultaneous comparative test on- and off-the-microfluidics using 12 negative and positive clinical samples. The outcomes yielded 100% accuracy in detecting the presence or absence of the SARS-CoV-2 virus, thus highlighting the potential of our PRCM system for multiplexed point-of-care immunoassays.

Video Transcripts Summarization using OpenAI Whisper and GPT Model

Abstract: In today’s digital age, a vast amount of video content is generated and shared on the internet every minute. However, extracting relevant information from these videos can be time-consuming and challenging. This is where video transcript summarization comes in, providing a concise summary of video content without the need to watch the entire video. The video transcript summarization system aims to streamline the process of extracting key insights and information from video content by generating concise and informative summaries from their transcripts. In the dynamic landscape of video content, existing approaches to transcript summarization have encountered challenges that limit their effectiveness. Issues such as compromised accuracy, prolonged processing times and a restricted focus on YouTube's captioned videos have prompted the exploration of alternative solutions. This paper introduces a novel approach that tackles these limitations head-on, employing innovative technologies to redefine the landscape of video transcript summarization. To address the identified limitations, we propose an alternative strategy that hinges on the utilization of advanced technologies like OpenAI Whisper Model Automatic Speech Recognition(ASR) for video’s Transcription and Generative Pre-trained Transformer (GPT) model for Summarization. Through the integration of the OpenAI Whisper model for transcription and GPT models for summarization, our proposed approach strives to redefine standards in accuracy, processing efficiency, multilingual support, and exhibit the capability to generate either extractive or abstractive summaries. In the context of natural language processing (NLP) and text summarization ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics will be used to evaluate the quality of generated summaries compared to human referenced summaries

Enhancing moisture detection in coal gravels: A deep learning-based adaptive microwave spectra fusion method

The accurate and effective detection of moisture in coal gravels is crucial. Conventional air oven-drying method suffers from prolonged processing times and their disruptive nature. This paper proposes a deep learning-based adaptive fusion method for multiple microwave spectra to non-destructively detect the moisture content of coal gravels. First, a purpose-built free-space measurement platform is employed to acquire microwave spectra of coal samples, encompassing the magnitude and phase spectra of reflection coefficients (S11) and transmission coefficients (S21). Subsequently, a Monte-Carlo cross-validation-based method is adopted to detect and eliminate outliers in the spectra. Furthermore, a novel feature extraction module is proposed, enhancing the traditional U-shaped network using residual learning (ResNet) and the convolutional block attention module (CBAM) to extract and reconstruct subtle spectral features. Inspired by the high-level data fusion, an adaptive spectra fusion method is then introduced that can autonomously balance the contributions between different spectra. The experimental results underscore the advantages of the proposed method, with narrow frequency intervals between 2.50–3.25 GHz, 3.75–4.00 GHz, and 4.75–5.00 GHz exhibiting superior detection accuracy compared to the entire frequency band, achieving R2 = 0.9034, MAE = 1.0254, RMSE = 1.2948 and RPIQ = 6.0630.

Evaluation of cracking risk of 80MnSi8-6 nanobainitic steel during hot forging in the range of lower temperature limits

Abstract Nanobainitic steels exhibit an exceptional combination of high strength, good plasticity, impact toughness, and wear resistance. They are suitable for the production of large mass components through the open-die forging process. Subsequently, the forgings are air-cooled. An obstacle of this method is the extended time required for the large forgings to undergo a bainitic transformation, making the industrial implementation of this process economically unjustifiable. Nevertheless, nanobainitic steels also allow for the open-die forging of small batches of structural elements with high property requirements. A technological limitation lies in the necessity of performing a series of operations, leading to a prolonged processing time dependent on the shape of the product and the degree of deformation. Therefore, inter-operational reheating is often necessary, incurring costs and time consumption. This is particularly relevant to forgings with a mass ranging from a few to several dozen kilograms, which, due to their low thermal capacity, rapidly dissipate heat to the surroundings and tools. Designing an economical process with a limited number of reheating cycles requires advanced knowledge of material behavior under thermo-mechanical deformation parameters, including boundary conditions where a significant decrease in plasticity occurs and the risk of crack initiation. To obtain this information, a comprehensive analysis of the influence of thermo-mechanical parameters applied during the deformation of nanobainitic steel at relatively low temperatures on the flow characteristics and crack formation was conducted. To achieve this goal, the Digital Image Correlation method, the finite element method modeling considering damage criteria, and the macrostructural evaluation of deformed specimens were employed.

Re-sequencing of the casein genes in Swedish Red cattle giving milk with diverse protein profiles and extreme rennet coagulation properties

Impaired rennet coagulation properties in milk could lead to prolonged processing times and production losses. Heritability for milk coagulation has previously been estimated to be 0.28 to 0.45, indicating that genetic selection can be used to manipulate this trait. The CN proteins are expressed by the genes CSN1S1, CSN2, CSN1S2, and CSN3 and are located on bovine chromosome 6. To better understand the effect of genetic variation in the CN genes on milk coagulation, blood and milk samples from 30 Swedish Red Dairy Cattle (RDC) with divergent coagulation properties were investigated. DNA from the 30 cows was sequenced for the CN genes to determine the theoretical AA sequence and to look for genetic variation in the untranslated regions. The aim is to confirm the protein genetic variants previously reported, while searching for additional genetic variation in the CN genes of 30 RDC. We observed genetic variation in 116 SNPs in the known CN genes where 10% of the SNPs are exon variants and the remaining 90% are intron variants. A total of 2.5% of the SNPs are found in the 5′- or 3′-untranslated region (UTR) regions of the exons; 2% are synonymous variants and 6% are missense variants that concurred with the known protein variants for CSN1S1, CSN2, and CSN3. Furthermore, 6% of the SNPs are splice polypyrimidine tract intron variants. The 2 genetic variants in the 5′- and 3′-UTR in CSN1S1 and CSN3 are found with protein variants CSN1S1C and CSN3B. Because both UTR variants are associated with gain and loss of micro RNA and transcription factors, this could explain differences in expression of the genetic protein variants. Preliminary chi-squared analysis and comparison with previous GWAS studies showed potential connections between the identified SNPs and coagulation properties of milk. By advancing the knowledge of the connection between the DNA sequence and the functional properties of the CN proteins, we hope to learn more about the cheese coagulation properties of milk from RDC.

Seamless Integration of Rapid Separation and Ultrasensitive Detection for Complex Biological Samples Using Multistage Annular Functionalized Carbon Nanotube Arrays.

Efficient separation, enrichment, and detection of bacteria in diverse media are pivotal for identifying bacterial diseases and their transmission pathways. However, conventional bacterial detection methods that split the separation and detection steps are plagued by prolonged processing times. Herein, a multistage annular functionalized carbon nanotube array device designed for the seamless integration of complex biological sample separation and multimarker detection is introduced. This device resorts to the supersmooth fluidity of the liquid sample in the carbon nanotubes interstice through rotation assistance, achieving the ability to quickly separate impurities and capture biomarkers (1mL sample cost time of 2.5 s). Fluid dynamics simulations show that the reduction of near-surface hydrodynamic resistance drives the capture of bacteria and related biomarkers on the functionalized surface of carbon nanotube in sufficient time. When further assembled as an even detection device, it exhibited fast detection (<30min), robust linear correlation (101-107 colony-forming units [CFU] mL-1, R2 = 0.997), ultrasensitivity (limit of detection = 1.7 CFU mL-1), and multitarget detection (Staphylococcus aureus, extracellular vesicles, and enterotoxin proteins). Collectively, the material and system offer an expanded platform for real-time diagnostics, enabling integrated rapid separation and detection of various disease biomarkers.