Individual Cells Research Articles

Bioprinting of artificial blood vessels.

Vascular networks have an important role to play in transporting nutrients, oxygen, metabolic wastes and maintenance of homeostasis. Bioprinting is a promising technology as it is able to fabricate complex, specific multi-cellular constructs with precision. In addition, current technology allows precise depositions of individual cells, growth factors and biochemical signals to enhance vascular growth. Fabrication of vascularized constructs has remained as a main challenge till date but it is deemed as an important stepping stone to bring organ engineering to a higher level. However, with the ever advancing bioprinting technology and knowledge of biomaterials, it is expected that bioprinting can be a viable solution for this problem. This article presents an overview of the biofabrication of vascular and vascularized constructs, the different techniques used in vascular engineering such as extrusion-based, droplet-based and laser-based bioprinting techniques, and the future prospects of bioprinting of artificial blood vessels.

Abstract We024: Spatial Transcriptomics Identifies a Regeneration-Permissive Microenvironment in the Neonatal Mammalian Heart

Ischemic heart disease is the leading cause of death globally, largely driven by inability of the adult heart to restore tissue structure and function after injury. However, the neonatal mouse heart is able to regenerate up to one week after birth, circumventing scarring and functional impairment. Neonatal heart regeneration is a spatially and temporally regulated process, involving coordinated communication of different cardiac cell types. Our previous work identified a regenerative cardiomyocyte population (CM4) that proliferates after myocardial infarction (MI), but the intercellular communication that induces their regenerative response is not fully understood. To study the cellular environment of CM4 cells during neonatal heart regeneration, we profiled spatial transcriptomes of regenerative P1 hearts at 3 days and 7 days after MI and compared them to nonregenerative P8 hearts. We identified unique spatial regions with distinct gene expression signatures, that we refer to as “molecular niches”, in the ischemic and border zones (IZ, BZ) of the P1 heart post-MI compared to P8 hearts. Cell type deconvolution of molecular niches showed a distinct temporal change of fibroblast and macrophage abundance in the IZ of P1 hearts compared to P8 hearts and also showed the localization of CM4 cells to the BZ of P1 hearts at 7 days post-MI. To identify intercellular communication that contributes to the differential injury response in P1 and P8 hearts, we analyzed ligand-receptor pairs that were expressed at higher levels in the IZ and BZ of P1 compared to P8 hearts. Among the nearly 50 signaling pairs identified, OSM and SLIT signaling have been previously implicated in heart regeneration. Notably, many of these signaling factors, including cytokines, chemokines and growth factors, have receptors expressed in multiple cardiac cell types. Thus, ongoing studies are systematically distilling the effect of each secreted factor on individual cell types in the heart using a cardiac cell co-culture and multiplexed single-cell RNA sequencing approach. In summary, our study provides insight into the spatial organization and tissue microenvironment of neonatal heart regeneration and identifies a number of candidate signaling pathways that potentially mediate critical intercellular communication during neonatal heart regeneration.

Dielectrophoretic capture and electrochemical enzyme-linked immunosorbent assay of single melanoma cells at an array of interlocked spiral bipolar electrodes.

Analysis of single cancer cells is critical to obtain accurate patient diagnosis and prognosis. In this work, we report the selective dielectrophoretic capture and electrochemical analysis of single melanoma cells at an array of interlocked spiral bipolar electrodes (iBPEs). Following dielectrophoretic capture, individual melanoma cells are hydrodynamically transferred into picoliter-scale chambers for subsequent analysis. The interlocked spiral end of the iBPE (the sensing pole) is utilized to read out an electrochemical enzyme-linked immunosorbent assay (eELISA), which quantifies the expression of a cell surface antigen, melanoma cell adhesion marker (MCAM). The opposite pole of each BPE is located in a fluidically isolated compartment containing reagents for electrogenerated chemiluminescence (ECL), such that luminescence reports iBPE current. In a preliminary device design, the ECL intensity was insufficient to detect MCAM expression on single cells. To achieve single-cell analysis, we decreased the gap size between the interlocked spirals tenfold (5.0 μm to 0.5 μm), thereby creating a more sensitive biosensor by enhanced redox cycling of the product of eELISA. This work is significant because it allows for the selective isolation and sensitive analysis of individual melanoma cells in a device amenable to point-of-care (POC) application by combining dielectrophoresis (DEP) with interdigitated bipolar electrodes (IDBPEs).

Novel mathematical approach to accurately quantify 3D endothelial cell morphology and vessel geometry based on fluorescently marked endothelial cell contours: Application to the dorsal aorta of wild-type and Endoglin-deficient zebrafish embryos.

Endothelial cells, which line the lumen of blood vessels, locally sense and respond to blood flow. In response to altered blood flow dynamics during early embryonic development, these cells undergo shape changes that directly affect vessel geometry: In the dorsal aorta of zebrafish embryos, elongation of endothelial cells in the direction of flow between 48 and 72 hours post fertilization (hpf) reduces the vessel's diameter. This remodeling process requires Endoglin; excessive endothelial cell growth in the protein's absence results in vessel diameter increases. To understand how these changes in vessel geometry emerge from morphological changes of individual endothelial cells, we developed a novel mathematical approach that allows 3D reconstruction and quantification of both dorsal aorta geometry and endothelial cell surface morphology. Based on fluorescently marked endothelial cell contours, we inferred cross-sections of the dorsal aorta that accounted for dorsal flattening of the vessel. By projection of endothelial cell contours onto the estimated cross-sections and subsequent triangulation, we finally reconstructed 3D surfaces of the individual cells. By simultaneously reconstructing vessel cross-sections and cell surfaces, we found in an exploratory analysis that morphology varied between endothelial cells located in different sectors of the dorsal aorta in both wild-type and Endoglin-deficient zebrafish embryos: In wild-types, ventral endothelial cells were smaller and more elongated in flow direction than dorsal endothelial cells at both 48 hpf and 72 hpf. Although dorsal and ventral endothelial cells in Endoglin-deficient embryos had similar sizes at 48 hpf, dorsal endothelial cells were much larger at 72 hpf. In Endoglin-deficient embryos, elongation in flow direction increased between 48 hpf and 72 hpf in ventral endothelial cells but hardly changed in dorsal endothelial cells. Hereby, we provide evidence that dorsal endothelial cells contribute most to the disparate changes in dorsal aorta diameter in wild-type and Endoglin-deficient embryos between 48 hpf and 72 hpf.

Redox heterogeneity in mouse embryonic stem cells individualizes cell fate decisions

Pluripotent embryonic stem cells (ESCs) can develop into any cell type in the body. Yet, the regulatory mechanisms that govern cell fate decisions during embryogenesis remain largely unknown. We now demonstrate that mouse ESCs (mESCs) display large natural variations in mitochondrial reactive oxygen species (mitoROS) levels that individualize their nuclear redox state, H3K4me3 landscape, and cell fate. While mESCs with high mitoROS levels (mitoROSHIGH) differentiate toward mesendoderm and form the primitive streak during gastrulation, mESCs, which generate less ROS, choose the alternative neuroectodermal fate. Temporal studies demonstrated that mesendodermal (ME) specification of mitoROSHIGH mESCs is mediated by a Nrf2-controlled switch in the nuclear redox state, triggered by the accumulation of redox-sensitive H3K4me3 marks, and executed by a hitherto unknown ROS-dependent activation process of the Wnt signaling pathway. In summary, our study explains how ESC heterogeneity is generated and used by individual cells to decide between distinct cellular fates.

Data acquisition approaches for single cell proteomics.

Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.

Accurate and rapid reactive flow simulations using dynamic load balancing and sparse analytical Jacobian approach

The present study introduces a rapid and accurate customized solver on the OpenFOAM platform for large-scale industrial computations. Specifically, a sparse analytical Jacobian approach utilizing the SpeedCHEM library was implemented to enhance the efficiency of the ordinary differential equation solver. The dynamic load balancing code was used to distribute computational workloads uniformly across multiple processes. Optimization continued with open multi-processing to improve parallel computing efficiency and the local time stepping scheme to maximize individual cell time steps. The effectiveness and robustness of the customized solver were first validated using Sandia flames D–F as benchmarks. The results showed that the customized solver exhibited better strong scaling characteristics and led to a speed increase of up to 30 times for two-dimensional Sandia flame D calculations. The numerical predictions for temperature and species distribution closely matched the experimental trends, confirming the accuracy of the solver. Subsequently, a three-dimensional numerical study on a 10 kW ammonia co-combustion furnace was conducted, exploring the performance of the solver in large-scale reactive simulations. Results analysis indicated that the acceleration capability was reduced due to increased communication overhead between processors, achieving up to 7.06 times speed-up. However, as the size of the reaction mechanism increases, better acceleration capabilities can be demonstrated. The numerical predictions could closely replicate experimental trends, effectively predicting NO emission trends within the combustion furnace. This study offers one viable solution for rapid and accurate calculations in the OpenFOAM platform, which could be applied in the subsequent ammonia industrial combustion processes.

Analyzing Thermal Runaway Propagation in Lithium-Ion Battery Modules with Reduced Flammability Electrolyte Cells

A thorough analysis of thermal runaway propagation was conducted, focusing on both module and cell levels, using a range of standard flammability and reduced flammability electrolytes in 18650 cells sourced from Eagle-Picher Technologies. Utilizing techniques such as accelerated rate calorimetry; cell enthalpy, maximum thermal runaway temperature, and thermal runaway onset and initiation temperatures were evaluated, consistently demonstrating their reliability. Employing the NASA X-57 module billet as a strategy for mitigating thermal runaway propagation, the absorption of cell energy during failure events was observed, effectively preventing the spread of thermal runaway between cells. Notably, the use of reduced flammability electrolyte resulted in an average reduction of 104 °C in maximum thermal runaway temperature at the module level, thereby offering decreased risk in preventing thermal runaway propagation during failures. Note the relatively modest 51 °C difference observed between reduced flammability and standard flammability cells during individual cell tests. This underscores the importance of conducting tests at both the cell and module levels in the development of systems for certification, where the cell-level data indicate a 51 °C discrepancy, while the module-level analysis shows a more substantial 104 °C differentiation.

A pan-cancer screen identifies drug combination benefit in cancer cell lines at the individual and population level

Combining drugs can enhance their clinical efficacy, but the number of possible combinations and inter-tumor heterogeneity make identifying effective combinations challenging, while existing approaches often overlook clinically relevant activity. We screen one of the largest cell line panels (N= 757) with 51 clinically relevant combinations and identify responses at the level of individual cell lines and tissue populations. We establish three response classes to model cellular effects beyond monotherapy: synergy, Bliss additivity, and independent drug action (IDA). Synergy is rare (11% of responses) and frequently efficacious (>50% viability reduction), whereas Bliss and IDA are more frequent but less frequently efficacious. We introduce "efficacious combination benefit" (ECB) to describe high-efficacy responses classified as either synergy, Bliss, or IDA. We identify ECB biomarkers invitro and show that ECB predicts response in patient-derived xenografts better than synergy alone. Our work here provides a valuable resource and framework for preclinical evaluation and the development of combination treatments.

Capture of circulating metastatic cancer cell clusters from lung cancer patients can reveal unique genomic profiles and potential anti-metastatic molecular targets: A proof-of-concept study.

Metastasis remains the leading cause of cancer deaths worldwide and lung cancer, known for its highly metastatic progression, remains among the most lethal of malignancies. Lung cancer metastasis can selectively spread to multiple different organs, however the genetic and molecular drivers for this process are still poorly understood. Understanding the heterogeneous genomic profile of lung cancer metastases is considered key in identifying therapeutic targets that prevent its spread. Research has identified the key source for metastasis being clusters of cells rather than individual cancer cells. These clusters, known as metastatic cancer cell clusters (MCCCs) have been shown to be 100-fold more tumorigenic than individual cancer cells. Unfortunately, access to these primary drivers of metastases remains difficult and has limited our understanding of their molecular and genomic profiles. Strong evidence in the literature suggests that differentially regulated biological pathways in MCCCs can provide new therapeutic drug targets to help combat cancer metastases. In order to expand research into MCCCs and their role in metastasis, we demonstrate a novel, proof of principle technology, to capture MCCCs directly from patients' whole blood. Our platform can be readily tuned for different solid tumor types by combining a biomimicry-based margination effect coupled with immunoaffinity to isolate MCCCs. Adopting a selective capture approach based on overexpressed CD44 in MCCCs provides a methodology that preferentially isolates them from whole blood. Furthermore, we demonstrate a high capture efficiency of more than 90% when spiking MCCC-like model cell clusters into whole blood. Characterization of the captured MCCCs from lung cancer patients by immunofluorescence staining and genomic analyses, suggests highly differential morphologies and genomic profiles. This study lays the foundation to identify potential drug targets thus unlocking a new area of anti-metastatic therapeutics.

Imputing abundance of over 2500 surface proteins from single-cell transcriptomes with context-agnostic zero-shot deep ensembles.

Cell surface proteins serve as primary drug targets and cell identity markers. The emergence of techniques like CITE-seq has enabled simultaneous quantification of surface protein abundance and transcript expression for multimodal data analysis within individual cells. The published data have been utilized to train machine learning models for predicting surface protein abundance based solely from transcript expression. However, the small scale of proteins predicted and the poor generalization ability for these computational approaches across diverse contexts, such as different tissues or disease states, impede their widespread adoption. Here we propose SPIDER (surface protein prediction using deep ensembles from single-cell RNA-seq), a context-agnostic zero-shot deep ensemble model, which enables the large-scale prediction of cell surface protein abundance and generalizes better to various contexts. Comprehensive benchmarking shows that SPIDER outperforms other state-of-the-art methods. Using the predicted surface abundance of >2500 proteins from single-cell transcriptomes, we demonstrate the broad applications of SPIDER including cell type annotation, biomarker/target identification, and cell-cell interaction analysis in hepatocellular carcinoma and colorectal cancer.

A real-time and energy-efficient SRAM with mixed-signal in-memory computing near CMOS sensors

In-memory computing (IMC) represents a promising approach to reducing latency and enhancing the energy efficiency of operations required for calculating convolution products of images. This study proposes a fully differential current-mode architecture for computing image convolutions across all four quadrants, intended for deep learning applications within CMOS imagers utilizing IMC near the CMOS sensor. This architecture processes analog signals provided by a CMOS sensor without the need for analog-to-digital conversion. Furthermore, it eliminates the necessity for data transfer between memory and analog operators as convolutions are computed within modified SRAM memory. The paper suggests modifying the structure of a CMOS SRAM cell by incorporating transistors capable of performing multiplications between binary (−1 or +1) weights and analog signals. Modified SRAM cells can be interconnected to sum the multiplication results obtained from individual cells. This approach facilitates connecting current inputs to different SRAM cells, offering highly scalable and parallelized calculations. For this study, a configurable module comprising nine modified SRAM cells with peripheral circuitry has been designed to calculate the convolution product on each pixel of an image using a 3×3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3 \ imes 3$$\\end{document} mask with binary values (−1 or 1). Subsequently, an IMC module has been designed to perform 16 convolution operations in parallel, with input currents shared among the 16 modules. This configuration enables the computation of 16 convolutions simultaneously, processing a column per cycle. A digital control circuit manages both the readout or memorization of digital weights, as well as the multiply and add operations in real-time. The architecture underwent testing by performing convolutions between binary masks of 3 × 3 values and images of 32 × 32 pixels to assess accuracy and scalability when two IMC modules are vertically integrated. Convolution weights are stored locally as 1-bit digital values. The circuit was synthesized in 180 nm CMOS technology, and simulation results indicate its capability to perform a complete convolution in 3.2 ms, achieving an efficiency of 11,522 1-b TOPS/W (1-b tera-operations per second per watt) with a similarity to ideal processing of 96%.

Detachable acoustofluidic droplet-sorter

BackgroundCell sorting is crucial in isolating specific cell populations. It enables detailed analysis of their functions and characteristics and plays a vital role in disease diagnosis, drug discovery, and regenerative medicine. Fluorescence-activated cell sorting (FACS) is considered the gold standard for high-speed single-cell sorting. However, its high cost, complex instrumentation, and lack of portability are significant limitations. Additionally, the high pressure and electric fields used in FACS can harm cell integrity. In this work, an acoustofluidic device was developed in combination with surface acoustic wave (SAW) and droplet microfluidics to isolate single-cell droplets with high purity while maintaining high cell viability. ResultHuman embryonic kidney cells, transfected with fluorescent reporter plasmids, were used to demonstrate the targeted droplet sorting containing single cells. The acoustofluidic sorter achieved a recovery rate of 81 % and an accuracy rate higher than 97 %. The device maintained a cell viability rate of 95 % and demonstrated repeatability over 20 consecutive trials without compromising efficiency, thus underscoring its reliability. Thermal image analysis revealed that the temperature of the interdigital transducer (IDT) during SAW operation remained within the permissible range for maintaining cell viability. SignificanceThe findings highlighted the sensitivity and effectiveness of the developed acoustofluidic device as a tool for single-cell sorting. The detachable microfluidic chip design enables the reusability of the expensive IDT, making it cost-effective and reducing the risk of cross-contamination between different biological samples. The results underscore its capability to accurately isolate individual cells on the basis of specific criteria, showcasing its potential to advance research and clinical applications requiring precise cell sorting methodologies.

HIV-1 Vif disrupts phosphatase feedback regulation at the kinetochore, leading to a pronounced pseudo-metaphase arrest.

The human immunodeficiency virus type 1 (HIV-1) Virion Infectivity Factor (Vif) targets and degrades cellular APOBEC3 proteins, key regulators of intrinsic and innate antiretroviral immune responses, thereby facilitating HIV-1 infection. While Vif's role in degrading APOBEC3G is well-studied, Vif is also known to cause cell cycle arrest but the detailed nature of Vif's effects on the cell cycle has yet to be delineated. In this study, we employed high-temporal single-cell live imaging and super-resolution microscopy to monitor individual cells during Vif-induced cell cycle arrest. Our findings reveal that Vif does not affect the G2/M boundary as previously thought. Instead, Vif triggers a unique and robust pseudo-metaphase arrest, which is markedly distinct from the mild prometaphase arrest induced by the HIV-1 accessory protein, Vpr, known for modulating the cell cycle. During Vif-mediated arrest, chromosomes align properly to form a metaphase plate but later disassemble, resulting in polar chromosomes. Notably, unlike Vpr, Vif significantly reduces the levels of both Phosphatase 1 (PP1) and 2 (PP2) at kinetochores, which are key regulators of chromosome-microtubule interactions. These results reveal a novel function of Vif in kinetochore regulation that governs the spatial organization of chromosomes during mitosis.

Creation of a novel zebrafish model with low DHA status to study the role of maternal nutrition during neurodevelopment.

Docosahexaenoic acid (DHA), a dietary omega-3 fatty acid, is a major building block of brain cell membranes. Offspring rely on maternal DHA transfer to meet their neurodevelopmental needs, but DHA sources are lacking in the American diet. Low DHA status is linked to altered immune responses, white matter defects, impaired vision, and an increased risk of psychiatric disorders during development. However, the underlying cellular mechanisms involved are largely unknown, and advancements in the field have been limited by the existing tools and animal models. Zebrafish are an excellent model for studying neurodevelopmental mechanisms. Embryos undergo rapid external development and are optically transparent, enabling direct observation of individual cells and dynamic cell-cell interactions in a way that is not possible in rodents. Here, we create a novel DHA-deficient zebrafish model by 1) disrupting elovl2, a key gene in the DHA biosynthesis pathway, via CRISPR-Cas9 genome editing, and 2) feeding mothers a DHA-deficient diet. We show that low DHA status during development is associated with a small eye morphological phenotype and demonstrate that even the morphologically normal siblings exhibit dysregulated gene pathways related to vision and stress response. Future work using our zebrafish model could reveal the cellular and molecular mechanisms by which low DHA status leads to neurodevelopmental abnormalities and provide insight into maternal nutritional strategies that optimize infant brain health.

Optical Nanomotion Detection to Rapidly Discriminate between Fungicidal and Fungistatic Effects of Antifungals on Single-Cell Candida albicans.

Candida albicans is an emerging pathogen that poses a significant challenge due to its multidrug-resistant nature. There are two types of antifungal agents, fungicidal and fungistatic, with distinct mechanisms of action against fungal pathogens. Fungicidal agents kill fungal pathogens, whereas fungistatic agents inhibit their growth. The growth can be restored once the agent is removed and favorable conditions are established. Recognizing this difference is crucial as it influences treatment selection and infection prognosis. We present a technique based on optical nanomotion detection (ONMD) (i.e., observing the movement of the cells using an optical microscope) to discriminate rapidly between fungicidal (caspofungin) and fungistatic (fluconazole) drugs. The technique is based on the change in a yeast cell's nanomotion as a function of time during a two-hour treatment with the antifungal of interest followed by a one-hour growth period. The cells are entrapped in microwells in a microfluidic chip, which allows a quick exchange of growth medium and antifungal agent, enabling ONMD measurements on the same individual cells before and after treatment. This procedure permits to discriminate between fungicidal and fungistatic antifungals in less than 3 h, with single-cell resolution by observing if the nanomotion recovers after removing the treatment and reintroducing growth medium (YPD), or continues to drop. The simplicity of the approach holds promise for further development into a user-friendly device for rapid antifungal susceptibility testing (AFST), potentially being implemented in hospitals and medical centers worldwide in developed and developing countries.

Fibrinolytic Platelet Decoys Reduce Cancer Metastasis by Dissociating Circulating Tumor Cell Clusters.

During metastasis, circulating tumor cells (CTCs) can travel in the bloodstream as individual cells or clusters, associated with fibrin and platelets. Clusters have a higher metastatic potential due to their increased ability to withstand shear stress and arrest in small vessels. Moreover, CTC-platelet interaction protects CTCs from shear stress and immune detection. The objective of this project is to develop a fibrinolytic platelet system to leverage platelet-CTC interactions and dissociate CTC clusters. For this approach, tissue plasminogen activator (tPA) is loaded onto two modified platelet systems: platelet Decoys and lyophilized platelets. The activities of the systems are characterized using a Förster Resonance Energy Transfer-based assay and an angiogenic assay. Furthermore, the ability of the system to dissociate cancer cell clusters in vitro is assessed using light transmission aggregometry. The data demonstrates that the fibrinolytic platelets can maintain tPA activity, interact with CTCs, and dissociate cancer cell clusters. Finally, fibrinolytic platelets are assessed in vivo, demonstrating a decreased tumor load and increased survival with tPA-Decoy treatment, which is selected as the optimal treatment based on favorable in vitro results and in vivo trials. Therefore, this fibrinolytic platelet approach is a promising method for leveraging platelet-CTC interactions to disperse CTC clusters and reduce metastasis.

Fiduciary-Free Frame Alignment for Robust Time-Lapse Drift Correction Estimation in Multi-Sample Cell Microscopy.

When analyzing microscopic time-lapse observations, frame alignment is an essential task to visually understand the morphological and translation dynamics of cells and tissue. While in traditional single-sample microscopy, the region of interest (RoI) is fixed, multi-sample microscopy often uses a single microscope that scans multiple samples over a long period of time by laterally relocating the sample stage. Hence, the relocation of the optics induces a statistical RoI offset and can introduce jitter as well as drift, which results in a misaligned RoI for each sample's time-lapse observation (stage drift). We introduce a robust approach to automatically align all frames within a time-lapse observation and compensate for frame drift. In this study, we present a sub-pixel precise alignment approach based on recurrent all-pairs field transforms (RAFT); a deep network architecture for optical flow. We show that the RAFT model pre-trained on the Sintel dataset performed with near perfect precision for registration tasks on a set of ten contextually unrelated time-lapse observations containing 250 frames each. Our approach is robust for elastically undistorted and translation displaced (x,y) microscopic time-lapse observations and was tested on multiple samples with varying cell density, obtained using different devices. The approach only performed well for registration and not for tracking of the individual image components like cells and contaminants. We provide an open-source command-line application that corrects for stage drift and jitter.

Compare individual and combined bioretention systems on the reduction of thermal pollution from stormwater runoff

Urban surface temperatures are high in summertime, and thermal pollution caused by heat transfer from pavement to stormwater runoff is harmful to aquatic ecosystems. However, there is a lack of studies investigating the temperature change pattern during rainstorms and evaluating the effects of bioretention on dynamic characteristics of thermal pollution. Therefore, this study selected a 1.05 ha parking lot retrofitted with five individual bioretention cells in Beijing as the object to compare the temperature and volume of stormwater runoff before and after bioretention treatment. In the LID parking lot, the average EMT and EMXT (event maximum temperature) of runoff decreased by 2.28 °C and 4.18 °C, respectively, and the median percent thermal load reduction was 90.6%. Data analysis from 15 summer rainfall events showed that the sequence of factors affecting runoff EMT (event mean temperature) was average air temperature, max air temperature, max solar radiation, and rainfall peak 5-min intensity. Bioretention profoundly changed the thermal dynamic characteristics of stormwater runoff. Surface runoff temperatures generally showed a decreasing trend over time. The temperature change pattern of LID parking lot outflow was synchronized with that of the inflow and varied with different grades of precipitation. The probability of the peak temperature ahead of peak flow decreased from 80% to 53%, suggesting that 27% of the thermal first-flush effect of thermal pollution from the urban surface was alleviated by site-scale bioretention implementation. The site-scale bioretention combination had a lower effluent temperature and a higher thermal load reduction rate than single-scale solutions. These results fill the gap in research on the thermal pollution reduction process of bioretention. Furthermore, they can guide the optimization of bioretention design methods and strategies to protect urban water bodies from the stormwater runoff thermal pollution.

The promise and challenges of genomics-informed periodontal disease diagnoses.

Recent advances in human genomics and the advent of molecular medicine have catapulted our ability to characterize human and health and disease. Scientists and healthcare practitioners can now leverage information on genetic variation and gene expression at the tissue or even individual cell level, and an enormous potential exists to refine diagnostic categories, assess risk in unaffected individuals, and optimize disease management among those affected. This review investigates the progress made in the domains of molecular medicine and genomics as they relate to periodontology. The review summarizes the current evidence of association between genomics and periodontal diseases, including the current state of knowledge that approximately a third of the population variance of periodontitis may be attributable to genetic variation and the management of several monogenic forms of the disease can be augmented by knowledge of the underlying genetic cause. Finally, the paper discusses the potential utility of polygenic risk scores and genetic testing for periodontitis diagnosis now and in the future, in light of applications that currently exist in other areas of medicine and healthcare.