Search

OBJECTIVEFailure to close the ductus arteriosus immediately post-birth, patent ductus arteriosus (PDA), accounts for up to 10% of all congenital heart defects. Despite significant advances in PDA management options, including pharmacological treatment targeting the prostaglandin pathway, a proportion of patients fail to respond and must undergo surgical intervention. Thus, further refinement of the cellular and molecular mechanisms that govern vascular remodeling of this vessel is required.APPROACH AND RESULTSAs anticipated, single-cell RNA sequencing on the ductus arteriosus in mouse embryos at E18.5, P0.5, and P5, revealed broad transcriptional alterations in the endothelial, smooth muscle, and fibroblast cell compartments. Making use of these data sets, vimentin emerged as an interesting candidate for further investigation. Subsequent studies demonstrated that, in fact, mice with genetic deletion of vimentin fail to complete vascular remodeling of the ductus arteriosus, as per presence of a functional lumen.CONCLUSIONSThrough single-cell RNA-sequencing and by tracking closure of the ductus arteriosus postnatally in mice, we uncovered the unexpected contribution of vimentin in driving complete closure of the ductus arteriosus potentially through regulation of the Notch signaling pathway.HIGHLIGHTSSingle-cell RNA-sequencing on the ductus arteriosus at E18.5, P0.5, and P5 reveals how the ductus arteriosus undergoes drastic transcriptional changes at the single-cell level.Endothelial cells increase levels of Vimentin, Notch1 and Jag1 transcripts soon after birth (P0.5), concurrent with ductus arteriosus closure.Loss of vimentin, the major intermediate filament protein of endothelial cells, prevents proper permanent closure of the ductus arteriosus.

Extracellular vesicles (EVs) are membrane vesicles secreted by cells, playing critical roles in mediating intercellular communications for various physiological and pathological processes. Most of the EV analysis is currently performed at the bulk level, obscuring the origin of the EVs and diverse characteristics of the individual extracellular vesicle. Technologies to analyze the extracellular vesicles at the single-cell and single-vesicle levels are needed to evaluate EV comprehensively and decode the heterogeneity underlying EV secretion. Microfluidic platforms that could control and manipulate fluids at the microscale provide an efficient way to achieve the aims. Various microfluidics-based technologies are emerging to realize single-cell EV secretion analysis and single EV analysis, which would be summarized in this mini-review.

Long non-coding RNAs (LncRNAs) are a type of RNA with little or no protein-coding ability. Their length is more than 200 nucleotides. A large number of studies have indicated that lncRNAs play a significant role in various biological processes, including chromatin organizations, epigenetic programmings, transcriptional regulations, post-transcriptional processing, and circadian mechanism at the cellular level. Since lncRNAs perform vast functions through their interactions with proteins, identifying lncRNA-protein interaction is crucial to the understandings of the lncRNA molecular functions. However, due to the high cost and time-consuming disadvantage of experimental methods, a variety of computational methods have emerged. Recently, many effective and novel machine learning methods have been developed. In general, these methods fall into two categories: semisupervised learning methods and supervised learning methods. The latter category can be further classified into the deep learning-based method, the ensemble learning-based method, and the hybrid method. In this paper, we focused on supervised learning methods. We summarized the state-of-the-art methods in predicting lncRNA-protein interactions. Furthermore, the performance and the characteristics of different methods have also been compared in this work. Considering the limits of the existing models, we analyzed the problems and discussed future research potentials.

Bone is composed of dense and solid cortical bone and honeycomb-like trabecular bone. Although cortical bone provides the majority of mechanical strength for a bone, there are few studies focusing on cortical bone repair or regeneration. Osteons (the Haversian system) form structural and functional units of cortical bone. In recent years, emerging evidences have shown that the osteon structure (including osteocytes, lamellae, lacunocanalicular network, and Haversian canals) plays critical roles in bone mechanics and turnover. Therefore, reconstruction of the osteon structure is crucial for cortical bone regeneration. This article provides a systematic summary of recent advances in osteons, including the structure, function, turnover, and regenerative strategies. First, the hierarchical structure of osteons is illustrated and the critical functions of osteons in bone dynamics are introduced. Next, the modeling and remodeling processes of osteons at a cellular level and the turnover of osteons in response to mechanical loading and aging are emphasized. Furthermore, several bioengineering approaches that were recently developed to recapitulate the osteon structure are highlighted. IMPACT STATEMENT: This review provides a comprehensive summary of recent advances in osteons, especially the roles in bone formation, remodeling, and regeneration. Besides introducing the hierarchical structure and critical functions of osteons, we elucidate the modeling and remodeling of osteons at a cellular level. Specifically, we highlight the bioengineering approaches that were recently developed to mimic the hierarchical structure of osteons. We expect that this review will provide informative insights and attract increasing attentions in orthopedic community, shedding light on cortical bone regeneration in the future.

We report how Raman difference imaging provides insight on cellular biochemistry in vivo as a function of sub-cellular dimensions and the cellular environment. We show that this approach offers a sensitive diagnostic to address blood biochemistry at the cellular level. We examine Raman microscopic images of the distribution of the different hemoglobins in both healthy (discocyte) and unhealthy (echinocyte) blood cells and interpret these images using pre-calculated, accurate pre-resonant Raman tensors for scattering intensities specific to hemoglobins. These tensors are developed from theoretical calculations of models of the oxy, deoxy and met forms of heme benchmarked against the experimental visible spectra of the corresponding hemoglobins. The calculations also enable assignments of the electronic transitions responsible for the colour of blood: these are mainly ligand to metal charge transfer transitions., We assign the electronic transitions responsible for the colour of blood and present a Raman imaging diagnostic approach for individual blood cells.

Recent emergence of FTIR spectromicroscopy (micro-FTIR) as a dynamic spectroscopy for imaging to the study of biological chemistry has opened new possibilities for investigating in situ drug release, redox chemistry effects with biological molecules, DNA and drug interactions, membranes dynamic, and redox reaction with proteins at the single cell level. Micro-FTIR applied to metallodrugs have been playing important role in the last decade because of its great potential to achieve more robust and controlled pharmacological effects against several diseases, including cancer. An important aspect in the development of these drugs is to understand their cellular properties, such as uptake, accumulation, activity, and toxicity. In this review, we present the potential application of the micro-FTIR and its importance for studying metal-based drugs, highlighting the prospective of chemistry of living cells. We also present a bioimaging emphasis, which is therefore of high importance to localize the cellular processes, for the proper understanding of the mechanism of action.

Liver metastasis, the leading cause of colorectal cancer mortality, exhibits a highly heterogeneous and suppressive immune microenvironment. Here, we sequenced 97 matched samples by using single-cell RNA sequencing and spatial transcriptomics. Strikingly, the metastatic microenvironment underwent remarkable spatial reprogramming of immunosuppressive cells such as MRC1+ CCL18+ M2-like macrophages. We further developed scMetabolism, a computational pipeline for quantifying single-cell metabolism, and observed that those macrophages harbored enhanced metabolic activity. Interestingly, neoadjuvant chemotherapy could block this status and restore the antitumor immune balance in responsive patients, whereas the nonresponsive patients deteriorated into a more suppressive one. Our work described the immune evolution of metastasis and uncovered the black box of how tumors respond to neoadjuvant chemotherapy. Significance: We present a single-cell and spatial atlas of colorectal liver metastasis and found the highly metabolically activated MRC1+ CCL18+ M2-like macrophages in metastatic sites. Efficient neoadjuvant chemotherapy can slow down such metabolic activation, raising the possibility to target metabolism pathways in metastasis. This article is highlighted in the In This Issue feature, p. 1

Background: Hypertrophic cardiomyopathy (HCM) is a complex disease partly explained by the effects of individual gene variants on sarcomeric protein biomechanics. At the cellular level, HCM mutations most commonly enhance force production, leading to higher energy demands. Despite significant advances in elucidating sarcomeric structure–function relationships, there is still much to be learned about the mechanisms that link altered cardiac energetics to HCM phenotypes. In this work, we test the hypothesis that changes in cardiac energetics represent a common pathophysiologic pathway in HCM. Methods: We performed a comprehensive multiomics profile of the molecular (transcripts, metabolites, and complex lipids), ultrastructural, and functional components of HCM energetics using myocardial samples from 27 HCM patients and 13 normal controls (donor hearts). Results: Integrated omics analysis revealed alterations in a wide array of biochemical pathways with major dysregulation in fatty acid metabolism, reduction of acylcarnitines, and accumulation of free fatty acids. HCM hearts showed evidence of global energetic decompensation manifested by a decrease in high energy phosphate metabolites (ATP, ADP, and phosphocreatine) and a reduction in mitochondrial genes involved in creatine kinase and ATP synthesis. Accompanying these metabolic derangements, electron microscopy showed an increased fraction of severely damaged mitochondria with reduced cristae density, coinciding with reduced citrate synthase activity and mitochondrial oxidative respiration. These mitochondrial abnormalities were associated with elevated reactive oxygen species and reduced antioxidant defenses. However, despite significant mitochondrial injury, HCM hearts failed to upregulate mitophagic clearance. Conclusions: Overall, our findings suggest that perturbed metabolic signaling and mitochondrial dysfunction are common pathogenic mechanisms in patients with HCM. These results highlight potential new drug targets for attenuation of the clinical disease through improving metabolic function and reducing mitochondrial injury.

Understanding cellular metabolism (how cells use energy) can be key in treating a wide range of diseases, including vascular disease and cancer. Although many techniques can measure these processes in tens of thousands of cells, researchers have not been able to measure them at the single-cell level. Researchers have used a genetically encoded biosensor with artificial intelligence to measure glycolysis. (Process of converting glucose to energy, single endothelial cells, blood vessel cells). Keywords:Cancer; Cells; Tissues, Tumors; Prevention, Prognosis; Diagnosis; Imaging; Screening; Treatment; Management https://www.raftpubs.com/jca-chemistry/articles/jca_raft1016.php

Could enzymatic activities and their cooperative functions act as cellular temperature-sensing systems? This review introduces recent opto-thermal technologies for microscopic analyses of various types of cellular temperature-sensing system. Optical microheating technologies have been developed for local and rapid temperature manipulations at the cellular level. Advanced luminescent thermometers visualize the dynamics of cellular local temperature in space and time during microheating. An optical heater and thermometer can be combined into one smart nanomaterial that demonstrates hybrid function. These technologies have revealed a variety of cellular responses to spatial and temporal changes in temperature. Spatial temperature gradients cause asymmetric deformations during mitosis and neurite outgrowth. Rapid changes in temperature causes imbalance of intracellular Ca2+ homeostasis and membrane potential. Among those responses, heat-induced muscle contractions are highlighted. It is also demonstrated that the short-term heating hyperactivates molecular motors to exceed their maximal activities at optimal temperatures. We discuss future prospects for opto-thermal manipulation of cellular functions and contributions to obtain a deeper understanding of the mechanisms of cellular temperature-sensing systems.

Local anesthetic systemic toxicity (LAST) is a life-threatening event caused by elevated local anesthetic plasma concentration. It is often unrecognized or misdiagnosed. Peripartum women are at increased risk for toxicity due to pregnancy-related physiological changes. Rising serum drug levels can cause cellular level impairment of mitochondria and voltage-gated ion channels leading to a cascade of symptoms that can end in cardiac arrest. Local anesthetic systemic toxicity can mimic other maternal pathologies but may be considered if local anesthetics have been used. Published treatment guidelines for this event include lipid emulsion which is approved for use in pregnant women. We review LAST in the maternity care setting, published treatment protocols, management of maternity patients with toxicity, and recommendations to increase awareness among maternity care clinicians for this medical emergency.

Measurement of gene expression at the single-cell level has led to important advances in the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell gene expression approaches have shed light on the high level of transcriptional heterogeneity of individual cells, both at baseline and in response to experimental or environmental perturbations. We have developed a method for High-Content Imaging (HCI)-based quantification of transcript abundance at the single-cell level in primary human immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, which we abbreviate as hcHCR, combines the high sensitivity of the hybridization chain reaction (HCR) for the visualization of mRNA molecules in single cells, with the speed, scalability, and technical reproducibility of HCI. We first tested eight microscopy-compatible attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in a 384-well format and documented the ability of the method to detect increased or decreased transcript abundance at the single-cell level in thousands of cells for each experimental condition by HCI. Furthermore, we demonstrated the feasibility of multiplexing gene expression measurements by simultaneously assaying the abundance of two transcripts per cell, both at baseline and in response to an experimental stimulus. Finally, we tested the robustness of the assay to technical and biological variation. We anticipate that hcHCR will be a suitable and cost-effective assay for low- to medium-throughput chemical, genetic or functional genomic screens in primary human cells, with the possibility of performing personalized screens or screens on cells obtained from patients with a specific disease.

Objective To generate a comprehensive transcriptomic profile of cellular signifures and transcriptomes of human placenta in GDM by single-cell RNA-sequencing (scRNA-seq), and built a comprehensive cell atlas. We hope to reveal the molecular mechanism of pregnancy risk for GDM women. Design Scientific study. Setting University Hospital and laboratories. Population or Sample Pregnant women Methods 20 GDM women and 20 normal pregnant women were recruited in present study. ScRNA-seq were loaded on the Chromium Single Cell Controller Instrument (10×Genomics) to generate single cell gel beads in emulsions (GEMs). Results A total of 27,220 cells from placenta samples were obtained. First, with the cell-type-specific marker genes, we annotated 15 cell clusters into more than 9 different cell types. Second, beside the classcial markers, we also found some novel markers for distinguishing three kinds of trophoblast and subtypes, which cloud be confirmed by immunohistochemistry imaging. Third, we demonstrated the specific placental function in GDM by the bioinformatics analysis of differentially expressed genes, such as estrogen signaling pathway, natural killer cell mediated cytotoxicity down- regulated. Fourth, there are abundant ligand-receptor interactions between trophoblast and immune cells in the maternal fetal interface microenvironment, such as VEGFB-FLT1, MIF-EGFR, RPS19-C5AR1, SPP1-CD44These dysfunctional ligand-receptor interactions may play the important roles in the development of GDM. Conclusion This study provided the first cell-type-specific transcriptomic alterations GDM placenta from single cell level, exploded the cell identities and cell-type-specific marker genes in the human placenta. In addition, it demonstrated the features of placental function and cell interactions for GDM.

Classical in vivo infection models are oftentimes associated with speculation due to the many physiological factors that are unseen or not accounted for when analyzing experimental outputs, especially when solely utilizing the classic approach of tissue-derived colony-forming unit (CFU) enumeration. To better understand the steps and natural progression of bacterial infection, the pathophysiology of individual organs with which the bacteria interact in their natural course of infection must be considered. In this case, it is not only important to isolate organs as much as possible from additional physiological processes, but to also consider the dynamics of the bacteria at the cellular level within these organs of interest. Here, we describe in detail two models, ex vivo porcine liver and spleen coperfusion and a murine infection model, and the numerous associated experimental outputs produced by these models that can be taken and used together to investigate the pathogen-host interactions within tissues in depth.

The lung consists of a plethora of cellular components working in concert to protect us against a variety of pathogens. This unique complexity poses a challenge to traditional transcriptomic approaches as averaging out signals across an intricate organ dampens individual cell type contributions. Technologies that allow transcriptomic level measurements at a single cell level, such as single cell RNA sequencing (scRNAseq) have enabled an unbiased survey of the organ during homeostasis and disease, while preserving intricate cellular heterogeneity. The utility of scRNAseq in respiratory science has been widely embraced over the past few years with key discoveries including the identification of rare cell types, new cell states associated with lung diseases, and a system wide expression of key severe acute respiratory syndrome coronavirus 2 (SARS-Cov2) receptors. Global single cell consortiums such as the Human Cell Atlas and LungMap have provided collaborative platforms that are crucial for large scale analysis, and they have emerged as important facilitators to further our understanding of lung biology. The rapid evolution of this technology will enable us to broaden the restraints of our knowledge leading to exciting discoveries that we currently see impossible.

The ability to deposit single cells in nonrestrictive two-dimensional and three-dimensional environments with high accuracy and reproducibility has proven invaluable for innovative cell and tissue research, and is now entering a new stage of maturity through systems engineering and next-generation designs. Coupling computer-aided design and computer-aided machining with laser direct write systems enables researchers to create reproducible biological models with micron-level precision. In particular, matrix-assisted pulsed-laser evaporation direct write system, and variations, has demonstrated this enhanced bottom-up approach to fabricating single-cell level constructs. This technology showcases the advantages of laser-assisted, nozzle-free, and contact-free printing that avoid probabilistic printing methods. In this chapter, we illustrate the importance of single-cell deposition and introduce laser-assisted bioprinting technology capable of micron-resolution and accuracy. This chapter will also review the post-deposition cell viability and mechanistics of laser-assisted transfer of biological materials.

SummaryReconstruction of shapes and sizes of three-dimensional (3D) objects from two-dimensional (2D) information is an intensely studied subject in computer vision. We here consider the level of single cells and nuclei and present a neural network-based SHApe PRediction autoencoder. For proof-of-concept, SHAPR reconstructs 3D shapes of red blood cells from single view 2D confocal microscopy images more accurately than naïve stereological models and significantly increases the feature-based prediction of red blood cell types from F1 = 79.0% to F1 = 87.4%. Applied to 2D images containing spheroidal aggregates of densely grown human induced pluripotent stem cells, we find that SHAPR learns fundamental shape properties of cell nuclei and allows for prediction-based morphometry. Reducing imaging time and data storage, SHAPR will help to optimize and up-scale image-based high-throughput applications for biomedicine.

Glioblastoma is the most aggressive cancer originating in the brain with an average survival of 15 months. One of the characteristics of glioblastoma is the high level of intra-tumor heterogeneity (ITH), but the composition and complexity at the single-cell level is poorly understood. Here, we aimed to assess the effects and consequences of immune checkpoint inhibitor (ICI) on the cellular and molecular heterogeneity of glioblastoma tumors at the single cell level. In collaboration with the phase I trials unit at Rigshospitalet, we performed paired molecular analysis of glioma cells from primary and relapse surgery after ICI treatment. Samples were analyzed using single-cell RNA sequencing (scRNA-seq) as well as bulk RNA sequencing and whole exome DNA sequencing. In an effort to trace cellular lineages we developed and refined methods to a identify copy number changes using scRNA-seq. To this end, we identified clonal and subclonal tumor cell populations in each sample. We found high levels of ITH prior to treatment, both with respect to the glioblastoma subtype enrichment and the cell type-specific gene expression. Using expression-based cell-type classification, we found defined recurrent cell-type populations present at both surgery time points. The immune checkpoint treatment had consequences on the cellular phenotypes and proportions of tumor cells, suggesting a level of plasticity in the neoplastic cells. Moreover, we identified examples of clonal dynamics and sweeps following ICI treatment, pointing to potential treatment response and resistance in these populations. We additionally found a recurrent pattern of a small population showing high levels of cell cycle activation and simultaneously expressing markers of stem cell potential. In summary, we pursued single cell-focused analysis of ICI treated glioblastoma patients to study the cellular and molecular heterogeneity within and between glioblastoma patients, which pointed to recurrent patterns of cellular responses following ICI treatment.

Several studies with brain signals suggested that bottom-up and top-down influences are exerted through distinct frequency bands among visual cortical areas. It has been recently shown that theta and gamma rhythms subserve feedforward, whereas the feedback influence is dominated by the alpha-beta rhythm in primates. A few theoretical models for reproducing these effects have been proposed so far. Here we show that a simple but biophysically plausible two-network motif composed of spiking-neuron models and chemical synapses can exhibit feedforward and feedback influences through distinct frequency bands. Differently from previous studies, this kind of model allows us to study directed influences not only at the population level, by using a proxy for the local field potential, but also at the cellular level, by using the neuronal spiking series.

Advances in microscopy hold great promise for allowing quantitative and precise readouts of morphological and molecular phenomena at the single cell level in bacteria. However, the potential of this approach is ultimately limited by the availability of methods to perform unbiased cell segmentation, defined as the ability to faithfully identify cells independent of their morphology or optical characteristics. In this study, we present a new algorithm, Omnipose, which accurately segments samples that present significant challenges to current algorithms, including mixed bacterial cultures, antibiotic-treated cells, and cells of extended or branched morphology. We show that Omnipose achieves generality and performance beyond leading algorithms and its predecessor, Cellpose, by virtue of unique neural network outputs such as the gradient of the distance field. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism and on the segmentation of non-bacterial objects. Our results distinguish Omnipose as a uniquely powerful tool for answering diverse questions in bacterial cell biology.

Background One goal of expression data analysis is to discover the biological significance or function of genes that are differentially expressed. Gene Set Enrichment (GSE) analysis is one of the main tools for function mining that has been widely used. However, every gene expressed in a cell is valuable information for GSE for single-cell RNA sequencing (scRNA-SEQ) data and not should be discarded. Methods We developed the functional expression matrix (FEM) algorithm to utilize the information from all expressed genes. The algorithm converts the gene expression matrix (GEM) into a FEM. The FEM algorithm can provide insight on the biological significance of a single cell. It can also integrate with GEM for downstream analysis. Results We found that FEM performed well with cell clustering and cell-type specific function annotation in three datasets (peripheral blood mononuclear cells, human liver, and human pancreas).

Classic metabolomic methods have proven to be very useful to study functional biology and variation in the chemical composition of different tissues. However, they do not provide any information in terms of spatial localization within fine structures. Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) does and reaches at best a spatial resolution of 0.25 μm depending on the laser setup, making it a very powerful tool to analyze the local complexity of biological samples at the cellular level. Here, we intend to give an overview of the diversity of the molecules and localizations analyzed using this method as well as to update on the latest adaptations made to circumvent the complexity of samples. MALDI MSI has been widely used in medical sciences and is now developing in research areas as diverse as entomology, microbiology, plant biology, and plant–microbe interactions, the rhizobia symbiosis being the most exhaustively described so far. Those are the fields of interest on which we will focus to demonstrate MALDI MSI strengths in characterizing the spatial distributions of metabolites, lipids, and peptides in relation to biological questions.

Cardiac arrhythmias represent about 50% of the cardiovascular diseases which are the first cause of mortality in the world. Implantable medical devices play a major role for treating these arrhythmias. Nevertheless the leads induce an unwanted biological phenomenon called fibrosis. This phenomenon begins at a cellular level and is effective at a macroscopic scale causing tissue remodelling with a local modification of the active cardiac tissue. Fibrosis mechanism is complex but at the cellular level, it mainly consists in cardiac fibroblasts activation and differentiation into myofibroblasts. We developed a simplified in vitro model of cardiac fibrosis, with human cardiac fibroblasts whom differentiation into myofibroblasts was promoted with TGF-β1. Our study addresses an unreported impedance-based method for real-time monitoring of in vitro cardiac fibrosis. The objective was to study whether the differentiation of cardiac fibroblasts in myofibroblasts had a specific signature on the cell index, an impedance-based feature measured by the xCELLigence system. Primary human cardiac fibroblasts were cultured along 6 days, with or without laminin coating, to study the role of this adhesion protein in cultures long-term maintenance. The cultures were characterized in the presence or absence of TGF-β1 and we obtained a significant cell index signature specific to the human cardiac fibroblasts differentiation.

Viscosity and pH are two crucial microenvironment parameters, and their abnormal fluctuations at the cellular level can act as key indicators of various diseases diagnosis. Considering that the traditional viscosity/pH meters cannot work in the cellular environment, developing some viscosity/pH-sensitive fluorescent probes is urgently demanded. Herein, a novel dual-functional camphor-based fluorescent probe PN-SP for viscosity and pH detection was constructed based on spiropyran open/closed-ring switching strategy. PN-SP exhibited an intense green emission in response to alkaline pH while a bright red fluorescence was observed in high-viscosity media, which could avoid some problems caused by the use of multiple probes such as cross-talk spectrum and different localizations. The sensing performances of PN-SP were also demonstrated for monitoring viscosity and pH fluctuations in living HeLa cells by means of different emisson channels. Therefore, we believed that this work will provide guidance for designing some dual-functional fluorescent probes.

Exact location of an object is not required in some scenarios of indoor localization and all we need to know is the area or a cell where the object is present in a room or a hall. For instance, the object to be located is large and we are interested in knowing the locations of the cell in which the object lies, or we want to find the quadrant of a room in which the object of interest lies. For such cases, in this paper, we propose a novel 2-phase cell localization algorithm based on trilateration. In the first phase we find the path loss exponent for each anchor node using RSSIs and in second phase we divide the available space into virtual cells and find the location of the cell in which the object is present. We have conducted real testbed experiments using Wi-Fi and LoRa with different transmission powers and calculated the error. Results show that the proposed algorithm successfully performs cell localization and Wi-Fi based localization is better than that of LoRa. Moreover, we have also studied how the accuracy of localization is affected by cell size in both of the wireless protocol.

： Lithium-ion batteries (LIBs) play a vital role in portable electronic products, transportation and large-scale energy storage. However, the electrochemical performance of LIBs deteriorates severely at low temperatures, exhibiting significant energy and power loss, charging difficulty, lifetime degradation, and safety issue, which has become one of the biggest challenges for LIBs. This article aims to review challenges and limitations of the battery chemistry in low-temperature environments, as well as the development of low-temperature LIBs from cell level to system level. This review introduces feasible solutions to accelarate low-temperature kinetics by increasing the inherent reactivity from cell design and improving the external reaction temperature from heating techniques. In addition, real-time and accurate monitoring of the battery temperature for the battery thermal management, as well as the optimization of charging protocols and the online lithium-plating monitoring in battery management systems are outlined. In general, a systematic review of low-temperature LIBs is conducted in order to provide references for future research.

Bamboo is mainly composed of fiber and parenchyma cells, and the gradient fiber distribution structure endows bamboo with excellent mechanical properties and improves its heterogeneity. Little is known about the real-time distribution and distribution mechanism of water on the gradient structure. In this study, the real-time moisture content of primeval bamboo with different density and proportions of fiber cells was tested. The mathematical models were used to calculate the content and distribution of adsorbed water in monolayered and multilayered bamboo and the differences in hygroscopicity of gradient structures were analyzed at cell level, sub-cellular level and molecular scale The results showed that microfibrillar aggregates of parenchyma cells differ at the subcellular scale, and parenchyma cells were more sensitive to changes in ambient humidity than fibers. When the humidity was higher than 40%, the moisture content of the different parts of bamboo differed considerably. When the humidity was 90%, the moisture content of the Inner, Middle, and Outer were 28.81%, 25.57%, and 18.55% respectively. And the adsorption isotherm were analyzed using the Hailwood–Horrobin (H–H) models. The monolayer moisture content in Inner was between 3.00% -3.50%. Under a high humidity, the multilayer adsorbed water content of Inner, Middle, and Outer reached 20.40–23.30%, 17.50–20.40%, and 14.60–17.00%.

Soil pollution from emerging contaminants poses a significant threat to water resources management and food production. The development of numerical models to describe the reactive transport of chemicals in both soil and plant is of paramount importance to elaborate mitigation strategies. To this aim, in the present study, a multiscale biophysical model is developed to predict the fate of ionizable compound in the soil-plant continuum. The modeling framework connects a multi-organelles model to describe processes at the cell level with a semi-mechanistic soil-plant model, which includes the widely used Richards-based solver, HYDRUS. A Bayesian probabilistic framework is used to calibrate and assess the capability of the model in reproducing the observations from an experiment on the translocation of five pharmaceuticals in green pea plants. Results show satisfactory fitting performance and limited predictive uncertainty. The subsequent validation with the cell model indicates that the estimated soil-plant parameters preserve a physically realistic meaning, and their calibrated values are comparable with the existing literature values, thus confirming the overall reliability of the analysis. Model results further suggest that pH conditions in both soil and xylem play a crucial role in the uptake and translocation of ionizable compounds.

Blossom-end rot can cause serious yield and quality losses to processing tomato producers, fresh market growers, and home gardeners alike. Blossom-end rot is often associated with calcium deficiency, though salt stress and even high concentrations of calcium salts can also cause blossom-end rot. The goal of this study was to characterize the negative effects of excess calcium compared to inadequate calcium at the whole plant and cellular level. Four calcium chloride levels (0 ppm, 10 ppm, 100 ppm, and 500 ppm) were applied daily to highly blossom-end rot susceptible processing tomato plants, starting at first flowering. Fruit were harvested 21–22 days after pollination. Blossom-end rot incidence and severity were lowest in the 10 ppm treatment. Blossom-end rot incidence was highest in the 0 ppm treatment and blossom-end rot severity was significantly increased in 500 ppm and 0 ppm treatments compared to 10 ppm CaCl2. No significant difference was found in the calcium content of tomato fruit harvested from plants treated with various levels of calcium. The soil drainage solution from the 500 ppm treatment had the highest conductivity, which was nearly 10 times higher than that of the 0 ppm and 10 ppm treatments. Daily plant water use was reduced in the 500 ppm treatment. Application of moderate calcium to tomato plants effectively reduced blossom-end rot incidence and severity, but excess levels of calcium did not reduce incidence and further increased blossom-end rot severity compared to moderate calcium application. Calcium applied to immature tomato fruit pericarp discs also inhibited blossom-end rot symptom development; however, excess calcium did not induce blossom-end rot symptoms. These results indicate that excess calcium induces blossom-end rot development due to effects at the whole plant level, but not due to excess calcium in the fruit tissue.

Recent breakthroughs in neurobiology indicate that the time is ripe to understand how cellular-level mechanisms are related to conscious experience. Here, we highlight the biophysical properties of pyramidal cells, which allow them to act as gates that control the evolution of global activation patterns. In conscious states, this cellular mechanism enables complex sustained dynamics within the thalamocortical system, whereas during unconscious states, such signal propagation is prohibited. We suggest that the hallmark of conscious processing is the flexible integration of bottom-up and top-down data streams at the cellular level. This cellular integration mechanism provides the foundation for Dendritic Integration Theory, a novel neurobiological theory of consciousness.

Duck eggs are nutritious and healthy diet and their properties further increased after being preserved by alkaline treatments. Preserved eggs are widely used as functional food products most probably as an alkalescent food posing several benefits to human health. An alkaline treatment increased their functional properties as a functional food ingredient, as well as their anti-inflammatory and anti-cancer properties are also enhanced. This review was aimed to accumulatively review functional properties of preserved eggs as an excellent food ingredient and as an excellent anti-inflammatory food. Hence, we have deeply reviewed the functional properties of preserved eggs as a functional food product with emphasis on their improved functional properties while preservation. Furthermore, we have given deep insights into rationale for alkaline pH in cell and cancer microenvironment in order to get better understanding about the health benefits of alkalescent food. At last, the detailed knowledge about anti-inflammatory and anti-cancer role of preserved eggs as an alkalescent food has been discussed at cellular level which made us aware of its importance for regular intake in our daily life for better health. However, there is still a lack of knowledge about this aspect in the research field and might rarely reviewed; hence this review will be promising in providing deep insights and further ideas for research conductance.

AEMWE is a novel technology combining the advantages of the already recognized electrolyzer technologies, i.e. Proton Exchange Membrane WE (PEMWE) and Alkaline WE (AWE) with the promise to eliminate the disadvantages of both. This review presents an overall opinion of the current state of AEMWE technology, focusing on component’s performance, durability and overall operation while identifying the critical gaps in the technology. It presents our perspective on the requisite developments in AEMWE at the cell component level in order to become a viable technology amongst other electrolyzer technologies with the potential for widespread adoption and commercialization.