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Hybrid microfluidic systems that are composed of multiple different types of substrates have been recognized as a versatile and superior platform, which can draw benefits from different substrates while avoiding their limitations. This review article introduces the recent innovations of different types of low-cost hybrid microfluidic devices, particularly focusing on cost-effective polymer- and paper-based hybrid microfluidic devices. In this article, the fabrication of these hybrid microfluidic devices is briefly described and summarized. We then highlight various hybrid microfluidic systems, including polydimethylsiloxane (PDMS)-based, thermoplastic-based, paper/polymer hybrid systems, as well as other emerging hybrid systems (such as thread-based). The special benefits of using these hybrid systems have been summarized accordingly. A broad range of biological and biomedical applications using these hybrid microfluidic devices are discussed in detail, including nucleic acid analysis, protein analysis, cellular analysis, 3D cell culture, organ-on-a-chip, and tissue engineering. The perspective trends of hybrid microfluidic systems involving the improvement of fabrication techniques and broader applications are also discussed at the end of the review. [ABSTRACT FROM AUTHOR]

In recent years, bamboo has been well exploited in the pulp and paper industry. Moso bamboo (Phyllostachys edulis (Carriere) J. Houzeau; hereafter M), originated from China with a long history of cultivation as the most abundant resource with the widest distribution area among bamboo plants in China. In this study, Moso bamboo (M) and nine bamboo varieties were selected in the bamboo germplasm resources storage bank of the Anhui Taiping Experimental Station of ICBR. The characteristics of their cell structures were compared, and the differences were analyzed from a genetic perspective. The results showed that M had the highest fiber cell length, fiber cell width, length, and parenchyma width, while GJ showed the lowest of these measurements [P. edulis 'Kikko-chiku',G.H.Lai]. The fiber wall thickness of Q [P. edulis f. obliquinoda (Z.P.Wang et N.X.Ma) Ohrnberger] was the smallest, while its fiber lumen diameter was the highest in the group. The parenchyma wall thickness and parenchyma lumen diameter of Q were the smallest in the group. The fiber cells of M and Q had better flexibility, which is conducive to improving the tensile strength, break resistance, and folding resistance of paper made from these materials. SY and GJ may be more suitable for ornamental items because of their special appearances. The purpose of this study was to explore the genetic variation patterns of various cell structure indicators among Moso bamboo and its varieties, as well as to develop a strategy of bamboo growing and lumbering based on the local conditions, providing reference data for the utilization of non-woody forest resources. [ABSTRACT FROM AUTHOR]

Detecting vascular endothelial growth factor C (VEGF-C), a kind of tumor biomarker, is of significant clinical importance in evaluating the prognosis of patients with cancer. However, laboratory analyses are usually not suitable for point-of-care testing because they are expensive and time consuming. In response to these challenges, we fabricated an origami paper-based microfluidic electrochemical device. To improve the specificity of VEGF-C detection, nanocomposites, synthesized by new methylene blue (NMB), amino-functional single-walled carbon nanotubes (NH2-SWCNTs), and gold nanoparticles (AuNPs), were used to modify the surface of working electrodes. Results of electrochemical detection showed that the immunosensor had excellent linearity, ranging from 0.01 to 100 ng mL−1 (R2 = 0.988), and the limit of detection was 10 pg mL−1. To confirm the high specificity of the device under real-world conditions, we evaluated the device using clinical serum samples from our hospital. The results demonstrated that the device had an excellent performance and could provide a platform for real-time detection of cancers. Sun, Wang et al. report an origami paper-based immunosensor for the electrochemical detection of the VEGF-C biomarker in blood serum. The immunosensor is made by modifying the surface of working electrodes with new methylene blue, amino-functional single-walled carbon nanotubes, and gold nanoparticles and demonstrates excellent performance with a limit of detection in the range of picograms per milliliter. [ABSTRACT FROM AUTHOR]

The Journal of Microscopy has issued a call for papers for a special issue on "Microscopy and Infectious Diseases." The special issue will focus on host-pathogen interactions and pathogen structure. The issue will be guest edited by Dr. Leandro Lemgruber from the University of Glasgow and Mariana De Niz from Northwestern University. The journal welcomes submissions in the form of reviews, methods and protocols, or primary research articles. Additionally, the RMS is now administering membership for the Association of Clinical Electron Microscopists (ACEM), and interested individuals can renew or apply for membership through the RMS website. [Extracted from the article]

Recent progress in flexible and lightweight electrochemical sensor systems requires the development of paper-like electrode materials. Here, we report a facile and green synthesis of a new type of MnO 2 nanowires–graphene nanohybrid paper by one-step electrochemical method. This strategy demonstrates a collection of unique features including the effective electrochemical reduction of graphene oxide (GO) paper and the high loading of MnO 2 nanowires on electrochemical reduced GO (ERGO) paper. When used as flexible electrode for nonenzymatic detection of hydrogen peroxide (H 2 O 2 ), MnO 2 –ERGO paper exhibits high electrocatalytic activity toward the redox of H 2 O 2 as well as excellent stability, selectivity and reproducibility. The amperometric responses are linearly proportional to H 2 O 2 concentration in the range 0.1–45.4 mM, with a detection limit of 10 μM (S/N = 3) and detection sensitivity of 59.0 μA cm −2 mM −1 . These outstanding sensing performances enable the practical application of MnO 2 –ERGO paper electrode for the real-time tracking H 2 O 2 secretion by live cells macrophages. Therefore, the proposed graphene-based nanohybrid paper electrode with intrinsic flexibility, tailorable shapes and adjustable properties can contribute to the full realization of high-performance flexible electrode material used in point-of-care testing devices and portable instruments for in-vivo clinical diagnostics and on-site environmental monitoring. [ABSTRACT FROM AUTHOR]

A novel simple and readily synthesized turn-on fluorescent probe 4-(benzo[d]thiazol-2-yl)-1,3-phenylene bis(2-chloroacetate) ( BPBC ) for cysteine (Cys) was reported. This probe was designed based on an excited-state intramolecular proton transfer (ESIPT) dye: benzothiazole, and two chloroacetate groups present in benzothiazole as the reaction sites for Cys. It shows high selectivity and sensitivity for Cys over other amino acids including the similar structured homocysteine (Hcy) and glutathione (GSH). In addition, probe BPBC was successfully applied to bioimage intracellular Cys in living cells with low cytotoxicity. More importantly, a paper test strip system was developed with probe BPBC for Cys detection conveniently. [ABSTRACT FROM AUTHOR]

Despite rapid progresses in single-cell analysis technologies, efforts to control the three-dimensional microenvironment for single cell measurements have been lacking. Here, we report a simple method to incorporate threedimensional scaffolds, including polyvinylidene fluoride (PVDF) membranes and PVDF membrane replicated analog polydimethylsiloxane, into multiplexed single cell secretomic analysis platforms (including a microwell array and a single cell barcode microchip) to mimic the extracellular physical matrix and mechanical support for single cells. Applying this platform to brain tumor cell line U87 to investigate single cell protein secretion behavior on different substrates, we revealed that single cell protein secretions were regulated differently in three-dimensional (3D) microenvironments. This finding was further verified with intracellular cytokine staining, highlighting the significance of 3D single cell microenvironments. This new single cell biomimetic platform can be easily adaptable to other three-dimensional cell culture scaffolds or other single cell assays and may become a broadly applicable three-dimensional single cell analysis system to study the effect of microenvironment conditions on cellular functional heterogeneity in vitro. [ABSTRACT FROM AUTHOR]

Natural killer (NK) cells are immune cells that defend against viral infections and cancer and are used in cancer immunotherapies. Subpopulations of NK cells include CD56dim and CD56bright which either produce cytokines or cytotoxically kill cells directly. The absolute number and proportion of these cells in peripheral blood are tied to proper immune function. Current methods of cytokine detection and proportion of NK cell subpopulations require fluorescent dyes and highly specialized equipment, e.g., flow cytometry, thus rapid cell quantification and subpopulation analysis are needed in the clinical setting. Here, a smartphone-based device and a two-component paper microfluidic chip were used towards identifying NK cell subpopulation and inflammatory markers. One unit measured flow velocity via smartphone-captured video, determining cytokine (IL-2) and total NK cell concentrations in undiluted buffy coat blood samples. The other, single flow lane unit performs spatial separation of CD56dim and CD56bright and cells over its length using differential binding of anti-CD56 nanoparticles. A smartphone microscope combined with cloud-based machine learning predictive modeling (utilizing a random forest classification algorithm) analyzed both flow data and NK cell subpopulation differentiation. Limits of detection for cytokine and cell concentrations were 98 IU/mL and 68 cells/mL, respectively, and cell subpopulation analysis showed 89% accuracy. • First smartphone-based paper microfluidic cell chromatography that can identify cell subpopulation. • Machine learning predictive modeling for NK cell subpopulation differentiation. • Integration of both cell chromatography and flow rate analysis on a single platform. • Potential application to many other cytokines and cell subpopulation analyses. [ABSTRACT FROM AUTHOR]

Drug hypersensitivity reactions ( DHRs) are a matter of great concern, both for outpatient and in hospital care. The evaluation of these patients is complex, because in vivo tests have a suboptimal sensitivity and can be time-consuming, expensive and potentially risky, especially drug provocation tests. There are several currently available in vitro methods that can be classified into two main groups: those that help to characterize the active phase of the reaction and those that help to identify the culprit drug. The utility of these in vitro methods depends on the mechanisms involved, meaning that they cannot be used for the evaluation of all types of DHRs. Moreover, their effectiveness has not been defined by a consensus agreement between experts in the field. Thus, the European Network on Drug Allergy and Drug Allergy Interest Group of the European Academy of Allergy and Clinical Immunology has organized a task force to provide data and recommendations regarding the available in vitro methods for DHR diagnosis. We have found that although there are many in vitro tests, few of them can be given a recommendation of grade B or above mainly because there is a lack of well-controlled studies, most information comes from small studies with few subjects and results are not always confirmed in later studies. Therefore, it is necessary to validate the currently available in vitro tests in a large series of well-characterized patients with DHR and to develop new tests for diagnosis. [ABSTRACT FROM AUTHOR]

Paper-based cultures are an emerging platform for preparing three-dimensional (3D) tissue- and tumor-like structures. The ability to stack individual sheets of cell-containing paper affords a modular means of assembling structures with defined cellular compositions and microenvironments. These layered stacks are easily separated at the end of an experiment, providing spatially resolved populations of live cells for further analysis. Here we describe a workflow in which cell viability, drug penetration, and drug metabolism are quantified in a spatially resolved manner. Specifically, we mapped the distribution of the drug irinotecan and its bioactive metabolite SN38 in a colorectal cancer cell-containing stacked structure with liquid chromatography-mass spectrometry (LC-MS). This paper provides the first example of a 3D culture platform that quantifies viability and drug metabolism in a spatially resolved manner. Our data show that cells at the bottom of the stack are more drug-resistant than layers in contact with the culture medium, similar to cells in the nutrient-poor center of a proliferating tumor being more drug-resistant than the rapidly dividing cells at its periphery. The powerful combination of quantitative viability and drug metabolism measurements will enable future studies to determine the exact mechanism(s) of drug resistance in different regions of a tumor. [Display omitted] • LC-MS quantification of drug metabolism and viability in paper-based tumor models. • Intracellular SN38 accumulation over 96 h correlates with decreased cell viability. • Drug efficacy was highest in stack layers that support proliferating cells. • Drug concentration and metabolism decreases in hypoxic layers. • Quantitative analysis of tumor model indicates microenvironment-mediated resistance. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide, affecting millions each year. It emerges from the colon or rectum, parts of the digestive system, and is closely linked to both genetic and environmental factors. In CRC, genetic mutations such as APC, KRAS, and TP53, along with epigenetic changes like DNA methylation and histone modifications, play crucial roles in tumor development and treatment responses. This paper delves into the complex biological underpinnings of CRC, highlighting the pivotal roles of genetic alterations, cell death pathways, and the intricate network of signaling interactions that contribute to the disease's progression. It explores the dysregulation of apoptosis, autophagy, and other cell death mechanisms, underscoring the aberrant activation of these pathways in CRC. Additionally, the paper examines how mutations in key molecular pathways, including Wnt, EGFR/MAPK, and PI3K, fuel CRC development, and how these alterations can serve as both diagnostic and prognostic markers. The dual function of autophagy in CRC, acting as a tumor suppressor or promoter depending on the context, is also scrutinized. Through a comprehensive analysis of cellular and molecular events, this research aims to deepen our understanding of CRC and pave the way for more effective diagnostics, prognostics, and therapeutic strategies. Highlights: Colorectal cancer (CRC) is one of the leading causes of death among patients. CRC has been characterized with changes at the genetic and epigenetic factors. The molecular factors can be used as diagnostic and prognostic factors in CRC. The cell death mechanisms demonstrate dysregulation in CRC. Autophagy has aberrant activation in CRC and exerts dual function. [ABSTRACT FROM AUTHOR]

With the increasing focus on decarbonisation of the transport sector, it is imperative to consider routes to electrify vehicles beyond those achievable using lithium-ion battery technology. These include heavy goods vehicles and aerospace applications that require propulsion systems that can provide gravimetric energy densities, which are more likely to be delivered by fuel cell systems. While the discussion of light-duty vehicles is abundant in the literature, heavy goods vehicles are under-represented. This paper presents an overview of the electrochemical degradation of a proton exchange membrane fuel cell integrated into a simulated Class 8 heavy goods range-extender fuel cell hybrid electric vehicle operating in urban driving conditions. Electrochemical degradation data such as polarisation curves, cyclic voltammetry values, linear sweep voltammetry values, and electrochemical impedance spectroscopy values were collected and analysed to understand the expected degradation modes in this application. In this application, the proton exchange membrane fuel cell stack power was designed to remain constant to fulfil the mission requirements, with dynamic and peak power demands managed by lithium-ion batteries, which were incorporated into the hybridised powertrain. A single fuel cell or battery cell can either be operated at maximum or nominal power demand, allowing four operational scenarios: maximum fuel cell maximum battery, maximum fuel cell nominal battery, nominal fuel cell maximum battery, and nominal fuel cell nominal battery. Operating scenarios with maximum fuel cell operating power experienced more severe degradation after endurance testing than nominal operating power. A comparison of electrochemical degradation between these operating scenarios was analysed and discussed. By exploring the degradation effects in proton exchange membrane fuel cells, this paper offers insights that will be useful in improving the long-term performance and durability of proton exchange membrane fuel cells in heavy-duty vehicle applications and the design of hybridised powertrains. [ABSTRACT FROM AUTHOR]

Cell migration is known to be a fundamental biological process, playing an essential role in development, homeostasis, and diseases. This paper introduces a cell tracking algorithm named HFM-Tracker (Hybrid Feature Matching Tracker) that automatically identifies cell migration behaviours in consecutive images. It combines Contour Attention (CA) and Adaptive Confusion Matrix (ACM) modules to accurately capture cell contours in each image and track the dynamic behaviors of migrating cells in the field of view. Cells are firstly located and identified via the CA module-based cell detection network, and then associated and tracked via a cell tracking algorithm employing a hybrid feature-matching strategy. This proposed HFM-Tracker exhibits superiorities in cell detection and tracking, achieving 75% in MOTA (Multiple Object Tracking Accuracy) and 65% in IDF1 (ID F1 score). It provides quantitative analysis of the cell morphology and migration features, which could further help in understanding the complicated and diverse cell migration processes. [ABSTRACT FROM AUTHOR]

The development of micro- and nanotechnology for biomedical applications has defined the cutting edge of medical technology for over three decades, as advancements in fabrication technology developed originally in the semiconductor industry have been applied to solving ever-more complex problems in medicine and biology. These technologies are ideally suited to interfacing with life sciences, since they are on the scale lengths as cells (microns) and biomacromolecules (nanometers). In this paper, we review the state of the art in bionanotechnology and bioMEMS (collectively BNM), including developments and challenges in the areas of BNM, such as microfluidic organ-on-chip devices, oral drug delivery, emerging technologies for managing infectious diseases, 3D printed microfluidic devices, AC electrokinetics, flexible MEMS devices, implantable microdevices, paper-based microfluidic platforms for cellular analysis, and wearable sensors for point-of-care testing. [ABSTRACT FROM AUTHOR]

Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator's skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder's input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier's output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-19. [ABSTRACT FROM AUTHOR]

Monitoring microbial metabolism is vital for revealing the mechanism of disease related to microbial metabolism and providing guidance for biomanufacturing processes optimization. However, it remains a grand challenge to offer real‐time insights into microbial metabolism owing to the complex and dynamic process. In this paper, the recent advances and prospects of optical biosensors including the organic, genetic coding and inorganic optical biosensors are briefly described for real‐time monitoring of dynamic microbial metabolism. This paper points out that challenges remain in microbial heterogeneity. We believe that this work will inspire the application of developing new methods for single cell real‐time analysis. [ABSTRACT FROM AUTHOR]

Objectives: A bibliometric and knowledge-map analysis is used to explore hotspots' evolution and development trends in the CAR-T cell field. By looking for research hotspots and new topics, we can provide new clues and ideas for researchers in this field. Methods: The articles and reviews regarding CAR-T cells were retrieved and obtained from the Web of Science Core Collection (WOSCC) on October 28th, 2021. CtieSpace [version 5.8.R3 (64-bit)] and VOSviewer (version 1.6.17) were used to conduct the bibliometric and knowledge-map analysis. Results: 660 authors from 488 institutions in 104 countries/regions published 6,867 papers in 1,212 academic journals. The United States was absolutely in the leading position in this research field. The institution that contributed the most publications was the University of Pennsylvania. Carl H June published the most articles, while Shannon L Maude had the most co-citations. However, there was little cooperation between countries. After 2012, cooperation among various institutions was also small. The journals that published the most CAR-T cell-related papers were Frontiers in immunology and Cancers. Nevertheless, Blood and The New England Journal of Medicine were the most commonly co-cited journals. The most influential research hotspots were the research of CAR-T cells in hematological malignancies, the related research of cytokine release syndrome (CRS), CD19, and the anti-tumor activity and efficacy of CAR-T cells. The latest hotspots and topics included the study of CAR-T cells in solid tumors, universal CAR-T cells, CAR-NK cells, CD22, and anakinra (the IL-1 receptor antagonist). The research of CAR-T cells in solid tumors was a rapidly developing hot field. Emerging topics in this field mainly included the study of CAR-T cells in glioblastoma (related targets: IL13Rα2, EGFRvIII, and HER2), neuroblastoma (related target: GD2), sarcoma (related target: HER2), and pancreatic cancer (related target: mesothelin), especially glioblastoma. Conclusion: As an anti-tumor therapy with great potential and clinical application prospects, CAR-T cell therapy is still in a stage of rapid development. The related field of CAR-T cells will remain a research hotspot in the future. [ABSTRACT FROM AUTHOR]

The paper deals with a final experimental validation of three-dimensional Multiconductor Cell Analysis (MCA) developed by the Electric Energy Transmission Laboratory of the University of Padova. This final confirmation comes from the measurement campaign carried out on one of the longest (100 km) HVAC submarine three-core cables: the Sicily-Malta interconnector. The considered frequency measurements range from 0 to 40th harmonic order with both positive and zero sequence supplies. In this overall harmonic interval and with both supplies, the comparison between measurements and MCA results highlights a very good agreement (maximum differences of 10%). These differences are negligible if measurement uncertainties are considered. [ABSTRACT FROM AUTHOR]

In 1871, the Swiss physiological chemist Friedrich Miescher published the results of a detailed chemical analysis of pus cells, in which he showed that the nuclei of these cells contained a hitherto unknown phosphorus-rich chemical which he named 'nuclein' for its specific localisation. Published in German, 'Ueber Die Chemische Zusammensetzung Der Eiterzellen', [On the Chemical Composition of Pus Cells] Medicinisch-Chemische Untersuchungen (1871) 4: 441–60, was the first publication to describe DNA, and yet remains relatively obscure. We therefore undertook a translation of the paper into English, which, together with the original article, can be accessed via the following link https://doi.org/10.1017/S000708742000062X. In this paper, we offer some intellectual context for its publication and immediate reception. [ABSTRACT FROM AUTHOR]

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel algorithm to optimize the design of Reservoir Computing using Cellular Automata models for time series applications. Besides selecting the models' hyperparameters, the proposed algorithm particularly solves the open problem of Linear Cellular Automaton rule selection. The selection method pre-selects only a few promising candidate rules out of an exponentially growing rule space. When applied to relevant benchmark datasets, the selected rules achieve low errors, with the best rules being among the top 5% of the overall rule space. The algorithm was developed based on mathematical analysis of Linear Cellular Automaton properties and is backed by almost one million experiments, adding up to a computational runtime of nearly one year. Comparisons to other state-of-the-art time series models show that the proposed Reservoir Computing using Cellular Automata models have lower computational complexity and, at the same time, achieve lower errors. Hence, our approach reduces the time needed for training and hyperparameter optimization by up to several orders of magnitude. [ABSTRACT FROM AUTHOR]

A sapwood tree is a species in which the sapwood does not differ significantly from the heartwood and cannot be classified by shades of color. It is generally accepted that heartwood has a higher economic value than sapwood, but most of the studies related to heartwood formation have focused on heartwood trees, with less research on sapwood trees. In this paper, we take the sapwood tree Schima superba as the research object and analyze the physiological and biochemical changes in the process of heartwood formation by studying the anatomical structure of parenchyma cells, and then further explore the main categories of metabolites and compositional changes. The results showed that during heartwood formation, the parenchyma cells become inactive and the nucleus disappears, while at the same time, the storage substance starch is gradually degraded under the action of enzymes and transformed into secondary metabolites, which include terpenoids, phenols and alkaloids. The accumulation of white and colorless compounds in large quantities in the heartwood, which has some effect on the heartwood color, is an important reason why the heartwood in Schima superba shows normal formation but no difference in color from the sapwood. This study fills a gap in the mechanism of heartwood formation in sapwood trees. [ABSTRACT FROM AUTHOR]

Direct catalytic methanol fuel cells (DCMFCs) have been studied for several years for energy conversion. Less extensive is the investigation of their analytical properties. In this paper, we demonstrate that the behavior of both the discharge and charger curves of DCMFCs depends on the chemical composition of the solution injected in the fuel cell. Their discharge and charge curves, analyzed using a chemometric data fusion method named ComDim, enable the identification of various types of aliphatic alcohols diluted in water. The results also show that the identification of alcohols can be obtained from the first portion of the discharge and charge curves. To this end, the curves have been described by a set of features related to the slope and intercept of the initial portion of the curves. The ComDim analysis of this set of features shows that the identification of alcohols can be obtained in a time that is about thirty times shorter than the time taken to achieve steady-state voltage. [ABSTRACT FROM AUTHOR]

This paper focuses on the active cell balancing of lithium-ion battery packs. An improved single-input, multioutput, bi-switch flyback converter was proposed to achieve effective balancing. The proposed topology simplifies the control logic by utilising a single MOSFET switch for energy transfer and two complementary pulses to control the cell-selecting switches. The proposed topology can decrease the number of switching devices as well as the size and cost of the system. The bi-switch flyback converter eliminates the need for a separate buffer circuit to minimise leakage and electromagnetic inductance. Losses and energy efficiency were analysed at each end of the proposed topology. The appropriate MATLAB simulations investigated the balancing characteristics of various state of charge (SOC) imbalances. A comparison is made between the balancing speed and energy transfer efficiency of the proposed topology and a conventional topology that employs a multi-input and multi-output flyback converter in a static mode. The results of the MATLAB simulation were validated by the OPAL-RT (OP5700) real-time simulator. The balancing data of the proposed topology were compared using MATLAB simulation and real-time simulation. This work may reduce the time required to assemble and commission the hardware for the proposed topology's real-time implementation. [ABSTRACT FROM AUTHOR]

Motivation Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-cell RNA sequencing techniques to identify cell clusters, the information contained in mono-omic data is often limited, leading to suboptimal clustering performance. The emergence of single-cell multi-omics sequencing technologies enables the integration of multiple omics data for identifying cell clusters, but how to integrate different omics data effectively remains challenging. In addition, designing a clustering method that performs well across various types of multi-omics data poses a persistent challenge due to the data's inherent characteristics. Results In this paper, we propose a graph-regularized multi-view ensemble clustering (GRMEC-SC) model for single-cell clustering. Our proposed approach can adaptively integrate multiple omics data and leverage insights from multiple base clustering results. We extensively evaluate our method on five multi-omics datasets through a series of rigorous experiments. The results of these experiments demonstrate that our GRMEC-SC model achieves competitive performance across diverse multi-omics datasets with varying characteristics. Availability and implementation Implementation of GRMEC-SC, along with examples, can be found on the GitHub repository: https://github.com/polarisChen/GRMEC-SC. [ABSTRACT FROM AUTHOR]

Stomatal movement, initiated by specialized epidermal cells known as guard cells (GCs), plays a pivotal role in plant gas exchange and water use efficiency. Despite protocols existing for isolating GCs through proplasting for carrying out biochemical, physiological, and molecular studies, protocals for isolating GCs with their cell walls still intact have been lacking in the literature. In this paper, we introduce a method for the isolation of complete GCs from Vicia faba and show their membrane to remain impermeable through propidium iodide staining. This methodology enables further in-depth analyses into the cell wall composition of GCs, facilitating our understanding of structure-function relationship governing reversible actuation within cells. [ABSTRACT FROM AUTHOR]

Counting and analyzing of blood cells, as well as their subcellular structures, are indispensable for understanding biological processes, studying cell functions, and diagnosing diseases. In this paper, we combine digital holographic microscopy with cell segmentation guided by the Sobel operator using Dice coefficients for automatic threshold selection and aimed to automatic counting and analysis of blood cells in flow and different kinds of cells in the static state. We demonstrate the proposed method with automatic counting and analyzing rat red blood cells (RBCS) flowing in a microfluidic device, extracting quickly and accurately the size, concentration, and dry mass of the sample in a label-free manner. The proposed technique was also demonstrated for automatic segmentation of different cell types, such as COS7 and Siha. This method can help us in blood inspection, providing pathological information in disease diagnosis and treatment. [ABSTRACT FROM AUTHOR]

The comparative analysis of the cellular glass products manufactured by the non-conventional microwave heating method compared to those made on a large scale by conventional methods is presented in this paper. Applying the non-conventional method was carried out in recent years by authors of the current article. Four different testing versions were selected by the composition of the material mixture including the expanding agent as well as the operational parameters. SiC, CaCO3, and Si3N4 separately constituted the expanding agents used in the analyzed experiments. Also, fly ash, red iron oxide, borax, and MnO2 were other additional materials included into the mixture, also separately. The specific energy consumption of producing processes were very low under the conditions of using a microwave equipment at very small-scale (0.8 kW), there being the possibility of improving this parameter by up to 25 % by applying the method on an industrial-scale. [ABSTRACT FROM AUTHOR]

One of the main types of uses of cellular materials is for energy absorption and dissipation in applications, such as safety and packaging, to protect people and goods during impact situations. In such cases, the use of cellular materials is justified by their capacity to largely deform under limited loads. This is often achieved, alone or within energy absorbing structures, with the additional advantage of cheap components that are relatively simple to manufacture and assemble. As in most engineering applications, weight reduction is sought after and, as in the case of other materials, this objective can be attained by optimizing the use of the material. Optimization of a cellular material for energy absorption means obtaining an optimal mechanical characteristic that can be obtained by properly designing it in terms of the type of base material and cell properties. Cell properties are mainly related to density and their optimal selection can be made by means of energy criteria. The aim of the present paper is to discuss such optimality criteria based on what are termed efficiency diagrams to produce an effective design tool. Additionally, based on empiric observations on the behavior of several classes of polymeric foams, a simplified selection method is proposed to hasten the selection criteria. [ABSTRACT FROM AUTHOR]

In this paper, we propose for the first time a self-refreshing mechanism in a junctionless field-effect transistor (JLFET) based on one-transistor dynamic random-access memory (1T-DRAM) with a silicon-on-insulator (SOI) structure. The self-refreshing mechanism continuously creates holes by appropriately generating impact ionization during the holding process through the application of an appropriate operation bias voltage. This leads to self-refreshing, which prevents the recombination of holes. When using the self-refreshing mechanism for the proposed device, the sensing margins were 15.4 and 12.7 μA/μm at 300 and 358 K, respectively. Moreover, the device achieved an excellent performance retention time of >500 ms, regardless of the temperature of the 1T-DRAM with a single gate. Furthermore, cell disturbance analysis and voltage optimization were performed to evaluate the in-cell reliability of the proposed device. It also showed excellent performance in terms of energy consumption and writing speed. [ABSTRACT FROM AUTHOR]

3D printing technique for microfluidics provides a rapid and cost-effective manufacturing method in one step without considering the complex three-dimensional structures. Because of the layer-by-layer printing process, the microchannel was characterized with rough surface while lacking research on it. Currently, the surface roughness of microchannels is improved mainly by etching (solvent vapor or wet chemical etching), which has two disadvantages: material selectivity and channel openness. In this study, the Polydimethylsiloxane (PDMS) coating method was employed to improve the surface quality of 3D printed microchannels to address the problems mentioned above. The adhesion strength of PDMS on 3D printed substrate was tested. The method proposed in this paper not only reduced the surface roughness of the microchannel effectively (40 fold for circular microchannels) but also reduced the pressure drop of the PDMS coated microchannel by 63.7% and reduced the leakage rate of the 3D printed microvalve by 67%. Moreover, cell viability was tested to validate the feasibility of the method for biomedical and cell analysis applications. Due to the good adhesion properties of PDMS to most materials used in microfluidics, the surface treatment method proposed in this paper can be expanded to other rapid fabrication techniques for low-cost and high-quality microfluidics. [ABSTRACT FROM AUTHOR]

Background: Single-cell RNA sequencing (scRNA-seq) is a powerful tool for investigating cell abundance changes during tissue regeneration and remodeling processes. Differential cell abundance supports the initial clustering of all cells; then, the number of cells per cluster and sample are evaluated, and the dependence of these counts concerning the phenotypic covariates of the samples is studied. Analysis heavily depends on the clustering method. Partitioning Around Medoids (PAM or k-medoids) represents a well-established clustering procedure that leverages the downstream interpretation of clusters by pinpointing real individuals in the dataset as cluster centers (medoids) without reducing dimensions. Of note, PAM suffers from high computational costs and memory requirements. Results: This paper proposes a method for differential abundance analysis using PAM as a clustering method and negative binomial regression as a statistical model to relate covariates to cluster/cell counts. We used this approach to study the differential cell abundance of human endometrial cell types throughout the natural secretory phase of the menstrual cycle. We developed a new R package -scellpam-, that incorporates an efficient parallel C++ implementation of PAM, and applied this package in this study. We compared the PAM-BS clustering method with other methods and evaluated both the computational aspects of its implementation and the quality of the classifications obtained using distinct published datasets with known subpopulations that demonstrate promising results. Conclusions: The implementation of PAM-BS, included in the scellpam package, exhibits robust performance in terms of speed and memory usage compared to other related methods. PAM allowed quick and robust clustering of sets of cells with a size ranging from 70,000 to 300,000 cells. https://cran.r-project.org/web/packages/scellpam/index.html. Finally, our approach provides important new insights into the transient subpopulations associated with the fertile time frame when applied to the study of changes in the human endometrium during the secretory phase of the menstrual cycle. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of a cellular vehicle-to-everything (C-V2X) quarter-wavelength monopole antenna performance when mounted on full glass roof. Antenna gain measurements performed on a full glass roof exhibited a performance degradation in a linear average gain (LAG) of 8 dB compared to when the same antenna is mounted on a metallic ground plane. In addition, the antenna radiation pattern on the glass roof had deep nulls. Due to a lack of information about the electrical material properties and architecture of the full glass roof sample from the glass supplier for proprietary reasons, a series of gain measurements were performed for the C-V2X monopole when mounted on different glass material samples. The measurement findings suggested the existence of a metal layer in the glass roof sample. Based on this information, the antenna was simulated using a full-wave, three-dimensional electromagnetic field solver on the full glass sample with a low-emissivity (low-E) coating on the edges of the full glass roof. The simulation results showed acceptable agreement with the measurements. A practical solution is suggested to improve the C-V2X antenna performance on the full glass roof. Specific absorption ratio (SAR) analysis is conducted for the passengers in the front and rear seats due to the passenger radiation exposure from the C-V2X monopole antenna mounted on the glass roof. The SAR study showed that the effect of the roof-top C-V2X monopole antenna radiation on the passengers is negligible. [ABSTRACT FROM AUTHOR]

Biological parameters extracted from electrical signals from various body parts have been used for many years to analyze the human body and its behavior. In addition, electrical signals from cancer cell lines, normal cells, and viruses, among others, have been widely used for the detection of various diseases. Single-cell parameters such as cell and cytoplasmic conductivity, relaxation frequency, and membrane capacitance are important. There are many techniques available to characterize biomaterials, such as nanotechnology, microstrip cavity resonance measurement, etc. This article reviews single-cell isolation and sorting techniques, such as the micropipette separation method, separation and sorting system (dual electrophoretic array system), DEPArray sorting system (dielectrophoretic array system), cell selector sorting system, and microfluidic and valve devices, and discusses their respective advantages and disadvantages. Furthermore, it summarizes common single-cell electrical manipulations, such as single-cell amperometry (SCA), electrical impedance sensing (EIS), impedance flow cytometry (IFC), cell-based electrical impedance (CEI), microelectromechanical systems (MEMS), and integrated microelectrode array (IMA). The article also enumerates the application and significance of single-cell electrochemical analysis from the perspectives of CTC liquid biopsy, recombinant adenovirus, tumor cells like lung cancer DTCs (LC-DTCs), and single-cell metabolomics analysis. The paper concludes with a discussion of the current limitations faced by single-cell analysis techniques along with future directions and potential application scenarios. [ABSTRACT FROM AUTHOR]

This paper develops a new approach to the modeling and analysis of device-to-device (D2D) underlaying multi-tier cellular network for dense hotspot communications, which consists of macro base stations (MBSs), pico BSs (PBSs), femto BSs (FBSs). A typicl user equipment (UE) can work either in D2D mode or cellular mode. Considering the dense hotspot communications, this work employs Poisson point process (PPP) to model the locations of MBSs and PBSs, and uses Poisson cluster process (PCP) to model the ones of UEs and FBSs. The locations of PBSs are also modeled as the centers of hotspots, referred to as the centers of PCPs. UEs and FBSs cluster around the common parent process PBSs. To guard the cluster-edge UEs, the clustered-UE classification and modified fractional frequency reuse (FFR) are jointly used, by which both the UEs and FBSs are classified two sets, cluster-center UEs and cluster-edge UEs, cluster-center FBSs and cluster-edge FBSs, respectively. The total frequency band is divided into two orthogonal segments, one of which is shared by D2D devices, cluster-edge FBSs, and PBSs, and the other segment of which is shared by cluster-center FBSs and MBSs. For such clustered multi-tier network, by using the methods from PPP, PCP, and PHP, this paper presents a tractable approach for modeling and analyzing the performance of cellular and D2D networks and gives the statistical descriptions of the experienced interferences at a typical D2D or cellular receiver by using the approximated Poisson hole processes (PHP) theory. This yields the derivations of the coverage probabilities of both the D2D receivers and cellular destinations. In additon, during the analysis of cellular UEs, to derive the coverage probabilities, this paper specially constructs one UE association criterion as well as the derivations of both the association probabilities and the statistical descriptions of association distances for cluster-center and cluster-edge UEs. The simulations results exploit the effect of various network parameters on the network performance and give the insights in terms of the proposed schemes as well as the comparison between cluster-center and cluster-edge UEs. [ABSTRACT FROM AUTHOR]

Quantum-dot cellular automata (QCA) are based on the nanoscale and used as an alternative for Complementary Metal Oxide (CMOS) semiconductor technology. The main feature that attracts researchers in the VLSI domain of QCA is the ultra-dense structures with less power consumption and fewer transistors count. This paper presents a low energy dissipation 1-bit magnitude comparator using modified MV32 gates with minimum area. The simulation results of the proposed design have a value of 2.05 meV as total energy dissipation and 0.198 meV as average energy dissipation per cycle. Being at the nanoscale, the possibility of defects must be explored and analyzed. Therefore, the single missing cell analysis for the MV32 gate is carried out and presented in this paper. The hardware description language model is developed to analyze the faults caused by the single missing cell defects in the MV32 gate. At last, the proposed comparator is analyzed under the single missing cell defect to prove its fault tolerance. [ABSTRACT FROM AUTHOR]

Encapsulation of cells inside microfluidic droplets is central to several applications involving cellular analysis. Although, theoretically the encapsulation statistics are expected to follow a Poisson distribution, experimentally this may not be achieved due to lack of full control of the experimental variables and conditions. Therefore, there is a need for automatic detection of droplets and cell count enumeration within droplets so a process control feedback to adjust experimental conditions can be implemented. In this study, we use a deep learning object detector called You Only Look Once (YOLO), an influential class of object detectors with several benefits over traditional methods. This paper investigates the application of both YOLOv3 and YOLOv5 object detectors in the development of an automated droplet and cell detector. Experimental data was obtained from a microfluidic flow focusing device with a dispersed phase of cancer cells. The microfluidic device contained an expansion chamber downstream of the droplet generator, allowing for visualization and recording of cell-encapsulated droplet images. In the procedure, a droplet bounding box is predicted, then cropped from the original image for the individual cells to be detected through a separate model for further examination. The system includes a production set for additional performance analysis with Poisson statistics while providing an experimental workflow with both droplet and cell models. The training set is collected and preprocessed before labeling and applying image augmentations, allowing for a generalizable object detector. Precision and recall were utilized as a validation and test set metric, resulting in a high mean average precision (mAP) metric for an accurate droplet detector. To examine model limitations, the predictions were compared to ground truth labels, illustrating that the YOLO predictions closely matched with the droplet and cell labels. Furthermore, it is demonstrated that droplet enumeration from the YOLOv5 model is consistent with hand counted ratios and the Poisson distribution, confirming that the platform can be used in real-time experiments for cell encapsulation optimization. [ABSTRACT FROM AUTHOR]

Single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) data analysis refers to the use of appropriate methods to analyze the dataset generated by RNA-sequencing performed on the single-cell transcriptome. It usually contains three steps: normalization to eliminate the technical noise, dimensionality reduction to facilitate visual understanding and data compression and clustering to divide the data into several similarity-based clusters. In addition, the gene expression data contain a large number of zero counts. These zero counts are considered relevant to random dropout events induced by multiple factors in the sequencing experiments, such as low RNA input, and the stochastic nature of the gene expression pattern at the single-cell level. The zero counts can be eliminated only through the analysis of the scRNA-seq data, and although many methods have been proposed to this end, there is still a lack of research on the combined effect of existing methods. In this paper, we summarize the two kinds of normalization, two kinds of dimension reduction and three kinds of clustering methods widely used in the current mainstream scRNA-seq data analysis. Furthermore, we propose to combine these methods into 12 technology combinations, each with a whole set of scRNA-seq data analysis processes. We evaluated the proposed combinations using Goolam, a publicly available scRNA-seq, by comparing the final clustering results and found the most suitable collection scheme of these classic methods. Our results showed that using appropriate technology combinations can improve the efficiency and accuracy of the scRNA-seq data analysis. The combinations not only satisfy the basic requirements of noise reduction, dimension reduction and cell clustering but also ensure preserving the heterogeneity of cells in downstream analysis. The dataset, Goolam, used in the study can be obtained from the ArrayExpress database under the accession number E-MTAB-3321. [ABSTRACT FROM AUTHOR]

Analysis of water distribution networks (WDNs) is considered to be indispensable for water supply design. Over the last decades, several methods have been developed to formulate and solve the steady-state hydraulic analysis of WDNs. For large WDNs, modeling the networks is complex and takes a great deal of time, and using the traditional methods becomes practically inefficient. This paper presents a parallel paradigm based on cellular automata to accelerate the hydraulic analysis of WDNs. The structure of the computing system is generated according to the structure of the WDN using the concept of cellular automata. Two methods are proposed for solving the hydraulic models. The performance of the proposed approach was compared with that of EPANET 2.0 software for networks with different complexity and topologies. The results indicate that, despite requiring more iterations to converge, the proposed parallel algorithm can accelerate the hydraulic analysis as much as 105 times using the proposed methods. WDNs analysis under steady-state conditions is used widely in applications such as network design and leak detection in WDNs. In these applications, it is necessary to solve hydraulic equations of the WDN under different scenarios to obtain the desired answer. As the size of the WDN increases, the time required to solve the equations increases accordingly. This paper presents a parallel paradigm to accelerate the hydraulic analysis of WDNs. The results indicate that the proposed parallel algorithm can accelerate the hydraulic analysis, thus reducing the network design time and determining the location of leaks in the WDN with greater speed and accuracy. Consequently, more money, time, and water can be saved. In addition, this acceleration may allow engineers to find new ways to solve their problems, or even to provide solutions to problems that in the past could not be solved with existing facilities. [ABSTRACT FROM AUTHOR]

These papers use flow cytometry in a very wide field, from very small cell-derived particles to huge cells, such as myocytes, and different detection technologies from the conventional fluorescence to mass spectrometry. It is good to see that the I Journal of Clinical Medicine i has recently published several good papers, highlighting the special input of cell analysis using flow cytometry. FCM sensitivity can detect as low as 1 tumor cell out of 10 SP 6 sp leukocytes, as compared to 1 out of 20 by standard morphology, assuming the tumor cell has a Leukemia-associated immunophenotype (LAIP) that can be identified by immunodetection. In conclusion, flow cytometry has exceptional performances to analyze cells or cell satellites with exceptionally high precision, in their phenotypes diversity not excluding rare outliers, as well as in their functional status. [Extracted from the article]

This paper develops the analysis of properties of the cellular automata class introduced by the authors. It is assumed that the set of automaton cells is finite and forms a closed lattice, and there are two states for each automaton cell. We consider a new concept. This concept is the average velocity of a cellular automaton, which characterizes the average intensity of changes in the states of the automaton's cells for a given initial state. The automaton velocity is equal to 1 if the state of any cell changes at each step. The spectrum of average velocities of a cellular automaton is the set of average velocities for different initial states. Since the state space is finite, the automaton, starting from a certain moment of time, is in periodically repeating states of a cycle, and thus, the research of the velocity spectrum is related to the problem of studying the set of the automaton cycles. For elementary cellular automata, the introduced class consists of a subclass of automata such that the conjugation trasformation of an automaton is the automaton itself (Subclass A) or the reflection of the automaton (Subclass B). For this class, it is proved that the spectrum of the automaton contains the value v 0 if and only if the spectrum of the complementary automaton contains the value 1 − v 0 (the sum of the index of elementary cellular automaton and the complementary automaton is 255). For automata of Subclasses A and B, the set of cycles and the velocity spectrum are studied. For Subclass A, a theorem has been proved such that in accordance with this theorem, if two automata complementary to each other start evolving in the same initial state, then the sum of their average velocities is equal to 1. This theorem for Subclass A is generalized to cellular automata, invariant under the conjugation transformation, of more general type than elementary automata. Generalizations of the theorem have been given for the class of one-dimensional cellular automata with a neighborhood containing 2 r + 1 cells (the next state of the cell depends on the present states of this cell, r cells on the left and r cells on the right) and for some traditionally considered classes of two-dimensional automata. Some elementary cellular automata belonging to the class considered in the paper can be interpreted as transport models. The properties of the spectra for these automata are studied and compared with the properties of elementary cellular automata not invariant under the considered transformations and can also be interpreted as transport models. The analytical results obtained for these simple models can be used to study the qualitative properties and limiting behavior of more complex transport models. [ABSTRACT FROM AUTHOR]

Inertial focusing excels at the precise spatial ordering and separation of microparticles by size within fluid flows. However, this advantage, resulting from its inherent size-dependent dispersion, could turn into a drawback that challenges applications requiring consistent and uniform positioning of polydisperse particles, such as microfiltration and flow cytometry. To overcome this fundamental challenge, we introduce Dispersion-Free Inertial Focusing (DIF). This new method minimizes particle size-dependent dispersion while maintaining the high throughput and precision of standard inertial focusing, even in a highly polydisperse scenario. We demonstrate a rule-of-thumb principle to reinvent an inertial focusing system and achieve an efficient focusing of particles ranging from 6 to 30 μm in diameter onto a single plane with less than 3 μm variance and over 95% focusing efficiency at highly scalable throughput (2.4–30 mL h−1) – a stark contrast to existing technologies that struggle with polydispersity. We demonstrated that DIF could be applied in a broad range of applications, particularly enabling high-yield continuous microparticle filtration and large-scale high-resolution single-cell morphological analysis of heterogeneous cell populations. This new technique is also readily compatible with the existing inertial microfluidic design and thus could unleash more diverse systems and applications. [ABSTRACT FROM AUTHOR]

Chondrogenesis is a complex cellular process that involves the transformation of mesenchymal stem cells (MSCs) into chondrocytes, the specialised cells that form cartilage. In recent years, three-dimensional (3D) culture systems have emerged as a promising approach to studying cell behaviour and development in a more physiologically relevant environment compared to traditional two-dimensional (2D) cell culture. The use of these systems provided insights into the molecular mechanisms that regulate chondrogenesis and has the potential to revolutionise the development of new therapies for cartilage repair and regeneration. This study demonstrates the successful application of Raman microspectroscopy (RMS) as a label-free, non-destructive, and sensitive method to monitor the chondrogenic differentiation of bone marrow-derived rat mesenchymal stem cells (rMSCs) in a collagen type I hydrogel, and explores the potential benefits of 3D hydrogels compared to conventional 2D cell culture environments. rMSCs were cultured on 3D substrates for 3 weeks and their differentiation was monitored by measuring the spectral signatures of their subcellular compartments. Additionally, the evolution of high-density micromass cultures was investigated to provide a comprehensive understanding of the process and complex interactions between cells and their surrounding extracellular matrix. For comparison, rMSCs were induced into chondrogenesis in identical medium conditions for 21 days in monolayer culture. Raman spectra showed that rMSCs cultured in a collagen type I hydrogel are able to undergo a distinct chondrogenic differentiation pathway at a significantly higher rate than the 2D culture cells. 3D cultures expressed stronger and more homogeneous chondrogenesis-associated peaks such as collagens, glycosaminoglycans (GAGs), and aggrecan while manifesting changes in proteins and lipidic content. These results suggest that 3D type I collagen hydrogel substrates are promising for in vitro chondrogenesis studies, and that RMS is a valuable tool for monitoring chondrogenesis in 3D environments. [ABSTRACT FROM AUTHOR]

We combine two-photon-excited fluorescence microscopy and acoustofluidic trapping in a spherical microchamber to in vitro study cells and cell clusters three-dimensionally close to in vivo conditions. The two-photon microscopy provides the in-depth 3D analysis of the spherical microchamber dimensions as well as the positions of trapped samples therein with high spatial precision and high temporal resolution enabling even tracking of the fast moving particles. Furthermore, optical sectioning allows to gather information of individual cells in trapped cell clusters inside the chamber. We demonstrate real-time monitoring of osmosis in A549 lung cells and red blood cells as one possible biomedical application. The observed osmosis reduced the cell membrane diameter by approximately 4 μm in the A549 cells and by approximately 2 μm in the red blood cells. Our approach provides an important optical tool for future investigations of cell functions and cell–cell interactions avoiding wall-contact inside an acoustofluidic device. [ABSTRACT FROM AUTHOR]

Microfluidic chips have emerged as significant tools in cell culture due to their capacity for supporting cells to adopt more physiologically relevant morphologies in 3D compared with traditional cell culture in 2D. Currently, irreversible bonding methods, where chips cannot be detached from their substrates without destroying the structure, are commonly used in fabrication, making it challenging to conduct further analysis on cells that have been cultured on-chip. Although some reversible bonding techniques have been developed, they are either restricted to certain materials such as glass, or require complex processing procedures. Here, we demonstrate a simple and reversible polydimethylsiloxane (PDMS)–polystyrene (PS) bonding technique that allows devices to withstand extended operations while pressurized, and supports long-term stable cell cultures. More importantly, it allows rapid and gentle live cell extraction for downstream manipulation and characterization after long-term on-chip culturing, and even further subculturing. Our new approach could greatly facilitate microfluidic chip-based cell and tissue cultures, overcoming current analytical limitations and opening up new avenues for downstream uses of on-chip cultures, including 3D-engineered tissue structures for biomedical applications. [ABSTRACT FROM AUTHOR]

The demand for high-resolution imaging, and high sample throughputs in LA-ICP-MS experiments has led to developing rapid-response ablation cells with low dispersion. These cells can achieve short transient signals called single pulse responses (SPRs), whose width is in the single-digit millisecond range at 1% of the maximum. However, coupled with ICP-Q-MS, recording those short signals poses a problem due to the sequential measurement of selected m/z ratios. If more than one m/z ratio is targeted, the time resolution of quadrupole detection systems is insufficient for accurately determining short SPRs. This work focuses on utilizing rapid response ablation cells for the analysis of multiple elements with quadrupole-based detection systems in the SPR mode. To achieve this, an ArF-excimer laser equipped with a rapid-response ablation chamber is coupled to an ICP-MS with a short settling time of 0.2 ms and below. The two naturally occurring Ag-isotopes were analyzed in the NIST SRM 612 to optimize the dwell times for ideal data acquisition. The data shows that the ratio of the dwell time and the settling time, plays a crucial role. With the optimized parameters, the natural ratio of 107Ag and 109Ag within a 10 ms FW0.01M transient signal could be determined. This concept of LA-ICP-MS was then applied for depth profiling analysis of Al-doped SiC, a wide bandgap semiconductor. This procedure acquired depth profiles on 30 different sample locations in approximately 2 minutes with an exceptional depth resolution of 55 nm. [ABSTRACT FROM AUTHOR]

In this paper the sequence impedances of an existing HVAC insulated cable, protected by a steel reinforced concrete structure, are estimated. The paper aims at investigating if the presence of metallic protective structures significantly affects the sequence impedances of the line. The present analyses are carried out by applying both the multiconductor cell analysis and the finite element method in order to model the HVAC line with and without protective structure. The results arising from the use of the different modelling approaches are compared. [ABSTRACT FROM AUTHOR]