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The diketopyrrolopyrrole-based polymer (DPPT-TT) has been employed in organic power field effect transistors due to its exceptional off-state breakdown performance. The impact of organic semiconductor layer thickness on the breakdown performance has not been explored. In this study, we investigate the impact of DPPT-TT layer thickness on the breakdown voltage (BV) by fabricating organic field effect transistors (OFETs) with various DPPT-TT layer thicknesses. Our findings reveal that the devices' BV is a strong function of DPPT-TT layer thickness, and reducing the DPPT-TT layer thickness from 68 to 15 nm results in a decrease in BV from 291 to 86 V, attributed to the two-dimensional (2D) electric field crowding effect. An analytical model utilizing the 2D Poisson equation reveals an electric field at the DPPT-TT layer's surface. Thinner DPPT-TT layer exhibits larger electric field peak, leading to premature breakdown near the drain electrode. The relationship between breakdown electric field and DPPT-TT layer thickness was established by fitting the experimental data to the model, revealing an average BV error of only 8.8%. This phenomenon is validated to be ubiquitous in polymer based OFETs via DPPT-TT-based and P3HT-based devices. According to the proposed model, this 2D electric field crowding effect can be mitigated by adjusting the dielectric layer thickness (tD) and/or the dielectric material. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: We report herein an unprecedented N‐heterocyclic carbene‐catalyzed formal [3 + 3] annulation of ynals with N‐Ts indolin‐3‐ones under the oxidation condition affording the functionalized pyrano[3,2‐b]indol‐2‐ones. The alkynyl acylazoliums via the combination of a carbene with ynals in the presence of oxidate proved to be the important intermediates for the success of this transformation. This method features a broad substrate scope and mild conditions, including axially chiral skeletons with suitable substitutions. [ABSTRACT FROM AUTHOR]

This study elucidates the thermal-induced bandgap broadening (TBB) phenomenon in copolymer organic semiconductors (OSCs). From the perspective of device physics, the diketopyrrolopyrrole-based copolymer was employed as the semiconductor layer to fabricate a back-to-back Schottky junction structure. Characterization through the analysis of I–V curves enables the qualitative exploration of the correlation between bandgap and temperature in copolymer OSCs. Then, technology computer-aided design was utilized to explore the influence of thermal-induced bandgap broadening on the back-to-back Schottky I–V curves. Subsequently, the variable temperature UV-VIS-NIR absorption spectra of the copolymer OSCs were analyzed, providing quantitative evidence of the thermal-induced bandgap broadening phenomenon and confirming its recoverability. Through the research of the Schottky junction and absorption spectra, we verified the consistency of the TBB phenomenon in both the electrical and optical bandgaps. From the scanning electron microscope images of the copolymer OSC films, it is found that the thermal-induced bandgap broadening phenomenon is mainly caused by thermal expansion and increased disorder of copolymer molecules. This study highlights a physical phenomenon of copolymer OSCs that is different from most inorganic semiconductors, and such insight offers a theoretical perspective for the application and thermal stability investigation of copolymer devices under high temperature conditions. [ABSTRACT FROM AUTHOR]

The early identification of rotten potatoes is one of the most important challenges in a storage facility because of the inconspicuous symptoms of rot, the high density of storage, and environmental factors (such as temperature, humidity, and ambient gases). An electronic nose system based on an ensemble convolutional neural network (ECNN, a powerful feature extraction method) was developed to detect potatoes with different degrees of rot. Three types of potatoes were detected: normal samples, slightly rotten samples, and totally rotten samples. A feature discretization method was proposed to optimize the impact of ambient gases on electronic nose signals by eliminating redundant information from the features. The ECNN based on original features presented good results for the prediction of rotten potatoes in both laboratory and storage environments, and the accuracy of the prediction results was 94.70% and 90.76%, respectively. Moreover, the application of the feature discretization method significantly improved the prediction results, and the accuracy of prediction results improved by 1.59% and 3.73%, respectively. Above all, the electronic nose system performed well in the identification of three types of potatoes by using the ECNN, and the proposed feature discretization method was helpful in reducing the interference of ambient gases. [ABSTRACT FROM AUTHOR]

Understanding metabolic heterogeneity is critical for optimizing microbial production of valuable chemicals, but requires tools that can quantify metabolites at the single‐cell level over time. Here, longitudinal hyperspectral stimulated Raman scattering (SRS) chemical imaging is developed to directly visualize free fatty acids in engineered Escherichia coli over many cell cycles. Compositional analysis is also developed to estimate the chain length and unsaturation of the fatty acids in living cells. This method reveals substantial heterogeneity in fatty acid production among and within colonies that emerges over the course of many generations. Interestingly, the strains display distinct types of production heterogeneity in an enzyme‐dependent manner. By pairing time‐lapse and SRS imaging, the relationship between growth and production at the single‐cell level are examined. The results demonstrate that cell‐to‐cell production heterogeneity is pervasive and provides a means to link single‐cell and population‐level production. [ABSTRACT FROM AUTHOR]

Nowadays, the impact ionization coefficient in the avalanche breakdown theory is obtained using 1-D PN junctions or SBDs, and is considered to be a constant determined by the material itself only. In this paper, the impact ionization coefficient of silicon in a 2D lateral power device is found to be no longer a constant, but instead a function of the 2D coupling effects. The impact ionization coefficient of silicon that considers the 2D depletion effects in real-world devices is proposed and extracted in this paper. The extracted impact ionization coefficient indicates that the conventional empirical impact ionization in the Fulop equation is not suitable for the analysis of 2D lateral power devices. The veracity of the proposed impact ionization coefficient is validated by the simulations obtained from TCAD tools. Considering the complexity of direct modeling, a new prediction method using deep neural networks is proposed. The prediction method demonstrates 97.67% accuracy for breakdown location prediction and less than 6% average error for the impact ionization coefficient prediction compared with the TCAD simulation. [ABSTRACT FROM AUTHOR]

This study explores whether to introduce jump variation into asset allocation. Using the high-frequency data of constituents of the Dow Jones Industrial(DJI) 30 index, we construct global minimum variance portfolios based on two different covariation estimators: pre-averaged and pre-averaged truncated Hayashi-Yoshida estimators (PAYHE and PATHYE, respectively). By comparing the performance of two different portfolios consider jump or not, we find that eliminating jump variation yields a significantly higher Sharpe ratio, and results in a lower turnover and superior positions compared to incorporating the jump variation. Thesve improvements are also reflected in economic gains, and the huge economic gain mainly comes from the reduction in turnover. [ABSTRACT FROM AUTHOR]

Monitoring biosynthesis activity at single‐cell level is key to metabolic engineering but is still difficult to achieve in a label‐free manner. Using hyperspectral stimulated Raman scattering imaging in the 670–900 cm−1 region, localized limonene synthesis are visualized inside engineered Escherichia coli. The colocalization of limonene and GFP‐fused limonene synthase is confirmed by co‐registered stimulated Raman scattering and two‐photon fluorescence images. The finding suggests a limonene synthesis metabolon with a polar distribution inside the cells. This finding expands the knowledge of de novo limonene biosynthesis in engineered bacteria and highlights the potential of SRS chemical imaging in metabolic engineering research. [ABSTRACT FROM AUTHOR]

We study how market regimes can influence investors' behaviour under the framework of prospect theory. Two market regimes (bull and bear) are considered, which affect our investors' reference points and their loss-averse behaviours. Using both theory and empirical evidence, the paper shows the disposition effect in a bull market is stronger than that in a bear market. Investors learn from both domains (gain and loss) in a bear market and more from the gain domain in a bull market. These results also provide an explanation for asymmetric learning regarding the disposition effect. [ABSTRACT FROM AUTHOR]

High‐sensitivity chemical imaging offers a window to decipher the molecular orchestra inside a living system. Based on vibrational fingerprint signatures, coherent Raman scattering microscopy provides a label‐free approach to map biomolecules and drug molecules inside a cell. Yet, by near‐infrared (NIR) pulse excitation, the sensitivity is limited to millimolar concentration for endogenous biomolecules. Here, the imaging sensitivity of stimulated Raman scattering (SRS) is significantly boosted for retinoid molecules to 34 micromolar via electronic preresonance in the visible wavelength regime. Retinoids play critical roles in development, immunity, stem cell differentiation, and lipid metabolism. By visible preresonance SRS (VP‐SRS) imaging, retinoid distribution in single embryonic neurons and mouse brain tissues is mapped, retinoid storage in chemoresistant pancreatic and ovarian cancers is revealed, and retinoids stored in protein network and lipid droplets of Caenorahbditis elegans are identified. These results demonstrate VP‐SRS microscopy as an ultrasensitive label‐free chemical imaging tool and collectively open new opportunities of understanding the function of retinoids in biological systems. [ABSTRACT FROM AUTHOR]

In this work, the effects of the methacrylate monomers with various end groups (methyl, hydroxyl, epoxy, tetrahydrofuran, cyclohexane, benzene and siloxane) on the morphologies, electro-optical and mechanical properties of polymer dispersed liquid crystal (PDLC) composite films were systematically investigated. The polymer network morphologies of the samples prepared with the monomers butyl methacrylate, tetrahydrofurfuryl methacrylate, cyclohexyl methacrylate or benzyl methacrylate were observed, and interestingly the polymer balls and polymer ball-networks morphologies were formed by introducing the hydroxypropyl methacrylate and glycidyl methacrylate, respectively. Meanwhile, the films obtained the optimal electro-optical properties with low driving voltage (10.7 V), fast response time (5.0 ms). Finally, the largest shearing force of the prepared PDLC composite films was two to three times better than that of the pristine sample with 3-(trimethoxysilyl)propyl methacrylate (TPMA) monomer doping or surface treatment methods. Thus, this work offers a new way to optimise the overall performance of PDLC films for practical applications. [ABSTRACT FROM AUTHOR]

In this work, a series of liquid crystal molecules containing cyano-phenyl-ester was designed and synthesised. Subsequently, a novel cyano-phenyl-ester liquid crystals (CPE-LCs) with a wide temperature range from −30°C to 96.4°C were developed by mixturing these liquid crystal compositions. Then as the host materials, the CPE-LCs were used for preparing polymer-dispersed and polymer-stabilised cholesteric liquid crystals (PD&SCLC) composite films by a stepwise polymerisation. The morphologies and electro-optical properties of the as-made composite films were systematically investigated through varying the acrylate liquid crystalline polymerisable monomers content, chiral doping R1011 content, applied electric field intensity and polymerisation time. Indeed, the formation of homeotropically aligned anisotropic polymer networks (HAAPNs) enabled to reduce the driving voltage of PD&SCLC from 86.6 V to 22.2 V with a reasonable contrast ratio. This work provides a new method to optimise PD&SCLC composite films in terms of the host liquid crystals. [ABSTRACT FROM AUTHOR]

Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a "mother machine" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human intervention. Further, the algorithm is fast, with complete analysis of a typical frame containing ~150 cells taking <700msec. The framework is not constrained to a particular experimental set up and has the potential to generalize to time-lapse images of other organisms or different experimental configurations. These advances open the door to a myriad of applications including real-time tracking of gene expression and high throughput analysis of strain libraries at single-cell resolution. Author summary: Automated microscopy experiments can generate massive data sets, allowing for detailed analysis of cell physiology and properties such as gene expression. In particular, dynamic measurements of gene expression with time-lapse microscopy have proved invaluable for understanding how gene regulatory networks operate. However, image processing remains a key bottleneck in the analysis pipeline, typically requiring human intervention and a posteriori processing. Recently, machine learning-based approaches have ushered in a new era of rapid, autonomous image analysis. In this work, we use and repurpose the U-Net deep learning algorithm to develop an image processing pipeline that can not only accurately identify the location of cells in an image, but also track them over time as they grow and divide. As an application, we focus on multi-hour time-lapse movies of bacteria growing in a microfluidic device. Our algorithm is accurate and fast, with error rates near 1% and requiring less than a second to analyze a typical movie frame. This increase in speed and fidelity has the potential to open new experimental avenues, e.g. where images are analyzed on-the-fly so that experimental conditions can be updated in real time. [ABSTRACT FROM AUTHOR]

Background Mediastinoscope-assisted transhiatal esophagectomy (MATHE) is the most minimally invasive esophagectomy procedure. It is a more challenging procedure and more difficult to be popularized than thoracoscopic surgery. We developed a new MATHE operation mode that provides a clearer visual field and makes the procedures simpler. Methods A total of 48 patients with esophageal cancer were divided into a control group (n = 29) and a study group (n = 19). The control group underwent classic MATHE, while the study group received modified MATHE. We compared the two groups on operation time; intraoperative blood loss; blood transfusion amount; incidence rate of lung infection, recurrent laryngeal nerves (RLNs) injury, chylothorax, and anastomotic leakage; and upper mediastinal lymph node dissection. Results The study group was significantly better than the control group in operation time (273.94 min vs. 322.90 min, p < 0.05), intraoperative blood loss (46.84 mL vs. 68.97 mL, p < 0.05), and left paratracheal lymph node (No. 4 L) dissection rate (84.21% vs. 24.14%, p < 0.01). No significant differences were identified in the incidence rate of anastomotic leakage, lung complications, or RLNs injury between the two groups. Conclusion The modified MATHE is easier to perform. Modified MATHE is significantly superior to classic MATHE in operation time, intraoperative blood loss, and upper mediastinal lymph node dissection rate. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: We report herein an unprecedented N‐heterocyclic carbene‐catalyzed formal [3 + 3] annulation of ynals with N‐Ts indolin‐3‐ones under the oxidation condition affording the functionalized pyrano[3,2‐b]indol‐2‐ones. The alkynyl acylazoliums via the combination of a carbene with ynals in the presence of oxidate proved to be the important intermediates for the success of this transformation. This method features a broad substrate scope and mild conditions, including axially chiral skeletons with suitable substitutions. [ABSTRACT FROM AUTHOR]

Current nucleic acid-responsive DNA hydrogels face significant challenges, such as the requirement for high target concentrations, frequent redesigns, and increased costs, which limit their practical applications in biosensing. To address these issues, we developed a novel biosensing platform integrating a CRISPR/Cas12a system into an RCA-based DNA hydrogel. The hydrogel used in the platform could preencapsulate diverse signal molecules comprising GelRed, methylene blue, and gold nanoparticles, which were released upon Cas12a-mediated cleavage. This design enabled customizable signal output, including fluorescence, electrochemistry, and colorimetry, thereby ensuring the platform's adaptability to various detection scenarios. Our platform was highly specific for methicillin-resistant Staphylococcus aureus , with a mecA gene detection limit of 10 copies/μL, and provided fast and accurate results within 2 h for clinical samples. Hence, based on these advantages, the proposed biosensing platform exhibits promising application prospects in the field of nucleic acid detection.

Background: Reports on combined resection for synchronous lung lesions and esophageal cancer (CRLE) cases are rare and mostly individual cases. Furthermore, the feasibility of CRLE has always been a controversial topic. In the current study, the authors retrospectively analyzed the feasibility of CRLE and established an individualized prediction model for esophageal anastomotic leaks after CRLE by performing a multicenter retrospective study., Methods: Patients who underwent esophagectomy between January 2009 and June 2021 were extracted from a four-center prospectively maintained database, and those with CRLE at the same setting were matched in a 1:2 propensity score-matched (PSM) ratio to esophagectomy alone (EA) patients. A nomogram was then established based on the variables involved in multivariate logistic regression analysis. Internal validation of the nomogram was conducted utilizing Bootstrap resampling. Decision and clinical impact curve analysis were computed to assess the practical clinical utility of the nomogram. A prognosis analysis for CRLE and EA patients by Kaplan-Meier curves was conducted., Results: Of the 7152 esophagectomies, 216 cases of CRLE were eligible, and 1:2 ratio propensity score-matched EA patients were matched. The incidence of anastomotic leaks following CRLE increased significantly ( P =0.035). The results of the multivariate analysis indicated the leaks varied according to the type of lung resection (anatomic>wedge resection, P =0.016) and site of resected lobe (upper>middle/low lobe; P =0.027), and a nomogram was established to predict the occurrence of leaks accurately (area under the curve=0.786). Although no statistically significant difference in overall survival (OS) was observed in the CRLE group ( P =0.070), a trend toward lower survival rates was noted. Further analysis revealed that combined upper lobe anatomic resection was significantly associated with reduced OS ( P =0.027)., Conclusion: Our study confirms that CRLE is feasible but comes with a significantly increased risk of anastomotic leaks and a concerning trend of reduced survival, particularly when upper lobe anatomic resections are performed. These findings highlight the need for careful patient selection and surgical planning when considering CRLE., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Histological examination is crucial for cancer diagnosis, however, the labor-intensive sample preparation involved in the histology impedes the speed of diagnosis. Recently developed two-color stimulated Raman histology could bypass the complex tissue processing to generates result close to hematoxylin and eosin staining, which is one of the golden standards in cancer histology. Yet, the underlying chemical features are not revealed in two-color stimulated Raman histology, compromising the effectiveness of prognostic stratification. Here, we present a high-content stimulated Raman histology (HC-SRH) platform that provides both morphological and chemical information for cancer diagnosis based on un-stained breast tissues. Methods: By utilizing both hyperspectral SRS imaging in the C-H vibration window and sparsity-penalized unmixing of overlapped spectral profiles, HC-SRH enabled high-content chemical mapping of saturated lipids, unsaturated lipids, cellular protein, extracellular matrix (ECM), and water. Spectral selective sampling was further implemented to boost the speed of HC-SRH. To show the potential for clinical use, HC-SRH using a compact fiber laser-based stimulated Raman microscope was demonstrated. Harnessing the wide and rapid tuning capability of the fiber laser, both C-H and fingerprint vibration windows were accessed. Results: HC-SRH successfully mapped unsaturated lipids, cellular protein, extracellular matrix, saturated lipid, and water in breast tissue. With these five chemical maps, HC-SRH provided distinct contrast for tissue components including duct, stroma, fat cell, necrosis, and vessel. With selective spectral sampling, the speed of HC-SRH was improved by one order of magnitude. The fiber-laser-based HC-SRH produced the same image quality in the C-H window as the state-of-the-art solid laser. In the fingerprint window, nucleic acid and solid-state ester contrast was demonstrated. Conclusions : HC-SRH provides both morphological and chemical information of tissue in a label-free manner. The chemical information detected is beyond the reach of traditional hematoxylin and eosin staining and heralds the potential of HC-SRH for biomarker discovery., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Surgical resection remains the primary approach for treating colorectal cancer, which is among the prevalent types of cancers affecting the digestive system. Tumor-infiltrating lymphocyte (TIL) therapy has emerged as a prominent area of study in the field of tumor immunotherapy in recent times, with the potential to serve as a supplementary treatment for colorectal cancer. For this investigation, we employed single-cell sequencing data to assess the manifestation extent of miR-26a-5p exists in healthy colon tissue, tissue affected by colorectal cancer, and tissue adjacent to the tumor. According to our findings, tumor-infiltrating T lymphocytes express comparatively less miR-26a-5p in comparison to normal T lymphocytes, the role of it in modulating the function of tumor-infiltrating T lymphocytes is suggested. Studies on miR-26a-5p's involvement in tumor-infiltrating T lymphocytes is limited, despite previous evidence indicating its ability to facilitate the development and advancement of cancerous cells. As a result of our experiments, we concluded that miR-26a-5p hindered the PI3K/AKT/mTOR(PAM) signaling pathway, reducing the ability of CD8+ tumor-infiltrating cells eradicate tumors. Using bioinformatics tools, we utilized prediction methods to identify EP300 as the specific gene targeted by miR-26a-5p. Subsequent research understood that downregulation of EP300 counteracted the suppressive impact exerted by miR-26a-5p on the stimulation of PAM signaling pathway, while it also diminishes the viability and cytotoxicity of CD8+ tumor-infiltrating lymphocytes. Therefore, miR-26a-5p emerges as a compelling option for the effective control of TIL therapy.

Stimulated Raman scattering (SRS) microscopy has shown enormous potential in revealing molecular structures, dynamics, and couplings in complex systems. However, the sensitivity of SRS is fundamentally limited to the millimolar level due to shot noise and the small modulation depth. To overcome this barrier, we revisit SRS from the perspective of energy deposition. The SRS process pumps molecules to their vibrationally excited states. The subsequent relaxation heats up the surroundings and induces refractive index changes. By probing the refractive index changes with a laser beam, we introduce stimulated Raman photothermal (SRP) microscopy, where a >500-fold boost of modulation depth is achieved. The versatile applications of SRP microscopy on viral particles, cells, and tissues are demonstrated. SRP microscopy opens a way to perform vibrational spectroscopic imaging with ultrahigh sensitivity.

Metabolic reprogramming in a subpopulation of cancer cells is a hallmark of tumor chemoresistance. However, single-cell metabolic profiling is difficult because of the lack of a method that can simultaneously detect multiple metabolites at the single-cell level. In this study, through hyperspectral stimulated Raman scattering (hSRS) imaging in the carbon-hydrogen (C-H) window and sparsity-driven hyperspectral image decomposition, we demonstrate a high-content hSRS (h 2 SRS) imaging approach that enables the simultaneous mapping of five major biomolecules, including proteins, carbohydrates, fatty acids, cholesterol, and nucleic acids at the single-cell level. h 2 SRS imaging of brain and pancreatic cancer cells under chemotherapy revealed acute and adapted chemotherapy-induced metabolic reprogramming and the unique metabolic features of chemoresistance. Our approach is expected to facilitate the discovery of therapeutic targets to combat chemoresistance. This study illustrates a high-content, label-free chemical imaging approach that measures metabolic profiles at the single-cell level and warrants further research on cellular metabolism.

Traditional electrochemical measurements based on either current or potential responses only present the average contribution of an entire electrode's surface. Here, we present an electrochemical photothermal reflectance microscope (EPRM) in which a potential-dependent nonlinear photothermal signal is exploited to map an electrochemical process with sub-micron spatial resolution. By using EPRM, we are able to monitor the photothermal signal of a Pt electrode during the electrochemical reaction at an imaging speed of 0.3 s per frame. The potential-dependent photothermal signal, which is sensitive to the free electron density, clearly revealed the evolution of surface species on the Pt surface. Our results agreed well with the reported spectroelectrochemical techniques under similar conditions but with a much faster imaging speed. We further mapped the potential oscillation during the oxidation of formic acid on the Pt surface. The photothermal images from the Pt electrode well matched the potential change. This technique opens new prospects for real-time imaging of surface chemical reaction to reveal the heterogeneity of electrochemical reactivity, which enables broad applications to the study of catalysis, energy storage, and light harvest systems., Competing Interests: There is no conflict to declare., (This journal is © The Royal Society of Chemistry.)

As a stable and accurate biomarker, glycated hemoglobin (HbA1c) is clinically used to diagnose diabetes with a threshold of 6.5% among total hemoglobin (Hb). Current methods such as boronate affinity chromatography involve complex processing of large-volume blood samples. Moreover, these methods cannot measure HbA1c fraction at single-red blood cell (RBC) level, thus unable to separate the contribution from other factors such as RBC lifetime. Here, we demonstrate a spectroscopic transient absorption imaging approach that is able to differentiate HbA1c from Hb on the basis of their distinct excited-state dynamics. HbA1c fraction inside a single RBC is derived quantitatively through phasor analysis. HbA1c fraction distribution of diabetic blood is apparently different from that of healthy blood. A mathematical model is developed to derive the long-term blood glucose concentration. Our technology provides a unique way to study heme modification and to derive clinically important information void of bloodstream glucose fluctuation.

Spectroscopic stimulated Raman scattering (SRS) imaging generates chemical maps of intrinsic molecules, with no need for prior knowledge. Despite great advances in instrumentation, the acquisition speed for a spectroscopic SRS image stack is fundamentally bounded by the pixel integration time. In this work, we report three-dimensional sparsely sampled spectroscopic SRS imaging that measures ~20% of pixels throughout the stack. In conjunction with related work in low-rank matrix completion (e.g., the Netflix Prize), we develop a regularized non-negative matrix factorization algorithm to decompose the sub-sampled image stack into spectral signatures and concentration maps. This design enables an acquisition speed of 0.8 s per image stack, with 50 frames in the spectral domain and 40,000 pixels in the spatial domain, which is faster than the conventional raster laser-scanning scheme by one order of magnitude. Such speed allows real-time metabolic imaging of living fungi suspended in a growth medium while effectively maintaining the spatial and spectral resolutions. This work is expected to promote broad application of matrix completion in spectroscopic laser-scanning imaging., Competing Interests: The authors declare no conflict of interest.

Flow cytometry is one of the most important technologies for high-throughput single-cell analysis. Fluorescent labeling acts as the primary approach for cellular analysis in flow cytometry. Nevertheless, the fluorescent tags are not applicable to all cases, especially to small molecules, for which labeling may significantly perturb the biological functionality. Spontaneous Raman scattering flow cytometry offers the capability to non-invasively detect chemical contents of cells but suffers from slow data acquisition. In order to achieve label-free high-throughput single-particle analysis using Raman scattering, we developed a 32-channel multiplex stimulated Raman scattering flow cytometry (SRS-FC) technique that can measure chemical contents of single particles at a speed of 5 μs per Raman spectrum. Using mixed polymer beads, we demonstrate the discrimination of different particles at a throughput of up to 11,000 particles per second. This is a four orders of magnitude improvement in throughput compared to conventional spontaneous Raman flow cytometry. As a proof of concept, we show the differentiation of 3T3-L1 cells at different states by SRS-FC according to the difference in cellular chemical content. The SRS-FC technique opens new opportunities for high-throughput and high-content chemical analysis of live cells in a label-free manner.