Analog multiplexing of a laser clock and computational photon counting for fast fluorescence lifetime imaging microscopy.

The dynamic range and fluctuations of fluorescence intensities and lifetimes in biological samples are large, demanding fast, precise, and versatile techniques. Among the high-speed fluorescence lifetime imaging microscopy (FLIM) techniques, directly sampling the output of analog single-photon detectors at GHz rates combined with computational photon counting can handle a larger range of photon rates. Traditionally, the laser clock is not sampled explicitly in fast FLIM; rather the detection is synchronized to the laser clock so that the excitation pulse train can be inferred from the cumulative photon statistics of several pixels. This has two disadvantages for sparse or weakly fluorescent samples: inconsistencies in inferring the laser clock within a frame and inaccuracies in aligning the decay curves from different frames for averaging. The data throughput is also very inefficient in systems with repetition rates much larger than the fluorescence lifetime due to significant silent regions where no photons are expected. We present a method for registering the photon arrival times to the excitation using time-domain multiplexing for fast FLIM. The laser clock is multiplexed with photocurrents into the silent region. Our technique does not add to the existing data bottleneck, has the sub-nanosecond dead time of computational photon counting based fast FLIM, works with various detectors, lasers, and electronics, and eliminates the errors in lifetime estimation in photon-starved conditions. We demonstrate this concept on two multiphoton setups of different laser repetition rates for single and multichannel FLIM multiplexed into a single digitizer channel for real-time imaging of biological samples.

Temporally optimized and spectrally shaped hyperspectral coherent anti-Stokes Raman scattering microscopy.

Coherent anti-Stokes Raman scattering (CARS) microscopy offers label-free chemical contrasts based on molecular vibrations. Hyperspectral CARS (HS-CARS) microscopy enables comprehensive microscale chemical characterization of biological samples. Various HS-CARS methods have been developed with individual advantages and disadvantages. We present what we believe to be a new temporally optimized and spectrally shaped (TOSS) HS-CARS method to overcome the limitations of existing techniques by providing precise control of the spatial and temporal profiles of the excitation beams for efficient and accurate measurements. This method uniquely uses Fourier transform pulse shaping based on a two-dimensional spatial light modulator to control the phase and amplitude of the excitation beams. TOSS-HS-CARS achieves fast, stable, and flexible acquisition, minimizes photodamage, and is highly adaptable to a multimodal multiphoton imaging system.

[Plectin Promotes the Migration of Hepatocellular Carcinoma Cells Through Inducing F-actin Polymerization]. / Plectin通过诱导F-actin聚合增强肝癌细胞的迁移能力.

Objective: To explore the relationship between the expression of plectin and the migration of hepatocellular carcinoma (HCC) cells and to elucidate the molecular mechanisms by which plectin expression affects the migration of HCC cells. Methods: First of all, Western blot was performed to determine the expression of plectin in normal hepatocytes and HCC cells. Secondly, a plectin-downregulated HCC cell strain was established and the control group (shNC group) and shPLEC group were set up. Each group was divided into a vehicle control group (shNC+DMSO group or shPLEC+DMSO group) and a F-actin cytoskeleton polymerization inducer Jasplakinolide group (shNC+Jasp group or shPLEC+Jasp group). Western blot was performed to determine the expression of plectin and epithelial-mesenchymal transition (EMT)-related proteins, including N-cadherin, vimentin, and E-cadherin. HCC cell migration was evaluated by Transwell assay. KEGG (Kyoto Encyclopedia of Genes and Genomes) was used to analyze the signaling pathways related to plectin gene. The polymerization of F-actin was analyzed by immunofluorescence assay. Results: Compared with the normal hepatocytes, HCC cells showed high expression of plectin. Compared with those in the shNC group, the expression of plectin in the shPLEC group was decreased (P<0.05), the migration ability of HCC cells was weakened (P<0.05), and the EMT process was inhibited (with the expression of N-cadherin and vimentin being decreased and the expression of E-cadherin being increased) (P<0.05). KEGG analysis showed that the regulation of cytoskeletal F-actin was most closely associated with plectin and cytoskeletal F-actin depolymerized in the shPLEC group. After treatment with Jasplakinolide, an inducer of F-actin cytoskeleton polymerization, the migration ability of HCC cells in the shPLEC+Jasp group was enhanced compared with that of shPLEC+DMSO group (P<0.05) and the EMT process was restored (with the expression of N-cadherin and vimentin being increased and the expression of E-cadherin being decreased) (P<0.05). In addition, the polymerization of cytoskeletal F-actin in HCC cells was also restored. Conclusion: Plectin is highly expressed in HCC cells. Plectin promotes the migration and the EMT of HCC cells through inducing F-actin polymerization.

Stabilization of MOF (KAT8) by USP10 promotes esophageal squamous cell carcinoma proliferation and metastasis through epigenetic activation of ANXA2/Wnt signaling.

Dysregulation of MOF (also known as MYST1, KAT8), a highly conserved H4K16 acetyltransferase, plays important roles in human cancers. However, its expression and function in esophageal squamous cell carcinoma (ESCC) remain unknown. Here, we report that MOF is highly expressed in ESCC tumors and predicts a worse prognosis. Depletion of MOF in ESCC significantly impedes tumor growth and metastasis both in vitro and in vivo, whereas ectopic expression of MOF but not catalytically inactive mutant (MOF-E350Q) promotes ESCC progression, suggesting that MOF acetyltransferase activity is crucial for its oncogenic activity. Further analysis reveals that USP10, a deubiquitinase highly expressed in ESCC, binds to and deubiquitinates MOF at lysine 410, which protects it from proteosome-dependent protein degradation. MOF stabilization by USP10 promotes H4K16ac enrichment in the ANXA2 promoter to stimulate ANXA2 transcription in a JUN-dependent manner, which subsequently activates Wnt/ß-Catenin signaling to facilitate ESCC progression. Our findings highlight a novel USP10/MOF/ANXA2 axis as a promising therapeutic target for ESCC.

Influence of oil extraction on concentration distributions, migration, secondary formation and carcinogenic risk of NPAHs and OPAHs in air and soil in an oilfield development area in China.

Oil extraction leads to environmental pollution from the oilfields and dweller activities, however, knowledge of the concentration distributions, migration, secondary formation and toxicity of nitrated/oxygenated polycyclic aromatic hydrocarbons (N/OPAHs) in oilfield regions is limited. In this research, atmospheric and soil samples in 7 different location types in an important oil industrial base in China were gathered. The ΣNPAHs and ΣOPAHs in the air ranged from 0.05 to 2.47 ng/m3 and 0.14-22.72 ng/m3, respectively, and in soil ranged from 0.22 to 17.81 ng/g and 9.69-66.86 ng/g, respectively. Both NPAHs and OPAHs in the atmosphere exhibited higher concentrations during winter. The atmospheric NPAH concentrations decreased exponentially with distance from urban area especially in the summer, revealing the impact of vehicles on the air in the Yellow River Delta area. High NPAH and OPAH concentrations were found only in soil near oil extraction facilities, indicating that the impact of oil extraction is limited to the soil near the extraction facilities. The air-soil exchanges of N/OPAHs were assessed through fugacity fraction analysis, and NPAHs were in the equilibrium-deposition state and OPAHs were in the net-deposition state in the winter. Higher incremental lifetime cancer risk (ILCR) occurred at the urban, industrial, and oilfield sites in the atmospheric samples, and the soil samples had the largest ILCR values in the oilfield sites. However, ILCR values for both air and soil did not exceed the threshold of 10-6.

Targeted inhibition of the HNF1A/SHH axis by triptolide overcomes paclitaxel resistance in non-small cell lung cancer.

Paclitaxel resistance is associated with a poor prognosis in non-small cell lung cancer (NSCLC) patients, and currently, there is no promising drug for paclitaxel resistance. In this study, we investigated the molecular mechanisms underlying the chemoresistance in human NSCLC-derived cell lines. We constructed paclitaxel-resistant NSCLC cell lines (A549/PR and H460/PR) by long-term exposure to paclitaxel. We found that triptolide, a diterpenoid epoxide isolated from the Chinese medicinal herb Tripterygium wilfordii Hook F, effectively enhanced the sensitivity of paclitaxel-resistant cells to paclitaxel by reducing ABCB1 expression in vivo and in vitro. Through high-throughput sequencing, we identified the SHH-initiated Hedgehog signaling pathway playing an important role in this process. We demonstrated that triptolide directly bound to HNF1A, one of the transcription factors of SHH, and inhibited HNF1A/SHH expression, ensuing in attenuation of Hedgehog signaling. In NSCLC tumor tissue microarrays and cancer network databases, we found a positive correlation between HNF1A and SHH expression. Our results illuminate a novel molecular mechanism through which triptolide targets and inhibits HNF1A, thereby impeding the activation of the Hedgehog signaling pathway and reducing the expression of ABCB1. This study suggests the potential clinical application of triptolide and provides promising prospects in targeting the HNF1A/SHH pathway as a therapeutic strategy for NSCLC patients with paclitaxel resistance. Schematic diagram showing that triptolide overcomes paclitaxel resistance by mediating inhibition of the HNF1A/SHH/ABCB1 axis.

Robust global arrangement by coherent enhancement in Huygens-Fresnel traveling surface acoustic wave interference field.

The application of standing surface acoustic wave (SSAW) tweezers based on backpropagation superposition to achieve precise behavior manipulation of microscale cells and even nanoscale bacteria has been widely studied and industrialized. However, the structure requires multiple transducer components or full channel resonance. It is very challenging to design a simple structure for nano-control by complex acoustic field. In this study, a reflector-interdigital transducer (R-IDT) acoustofluidic device based on unilateral coherence enhancement is proposed to achieve SSAW definition features of periodic particle capture positions. The SAW device based on a unilateral transducer can not only generate leaky-SAW in water-filled microchannel, but also have a contribution of spherical waves in the vibration area of the substrate-liquid interface due to the Huygens-Fresnel diffractive principle. Both of them form a robust time-averaged spatial periodicity in the pressure potential gradient, accurately predicting the lateral spacing of these positions through acoustic patterning methods. Furthermore, a reflector based on Bragg-reflection is used to suppress backward transmitted SAW and enhance forward conducted SAW beams. By using a finite element model, R-IDT structure's amplitude enhances 60.78% compared to single IDT structure. The particle manipulation range of the diffractive acoustic field greatly improves, verified by experimental polystyrene microspheres. Besides, biocompatibility is conformed through red blood cells and Bacillus subtilis. We investigate the overall shift of periodic pressure field that can still occur when the phase changes. This work provides a simpler and low-cost solution for the application of acoustic tweezer in biological cell culture and filtering.

Key toxic components and sources affecting oxidative potential of atmospheric particulate matter using interpretable machine learning: Insights from fog episodes.

Fog significantly affects the air quality and human health. To investigate the health effects and mechanisms of atmospheric fine particulate matter (PM2.5) during fog episodes, PM2.5 samples were collected from the coastal suburb of Qingdao during different seasons from 2021 to 2022, with the major chemical composition in PM2.5 analyzed. The oxidative potential (OP) of PM2.5 was determined using the dithiothreitol (DTT) method. A positive matrix factorization model was adopted for PM2.5. Interpretable machine learning (IML) was used to reveal and quantify the key components and sources affecting OP. PM2.5 exhibited higher oxidative toxicity during fog episodes. Water-soluble organic carbon (WSOC), NH4+, K+, and water-soluble Fe positively affected the enhancement of DTTV (volume-based DTT activity) during fog episodes. The IML analysis demonstrated that WSOC and K+ contributed significantly to DTTV, with values of 0.31 ± 0.34 and 0.27 ± 0.22 nmol min-1 m-3, respectively. Regarding the sources, coal combustion and biomass burning contributed significantly to DTTV (0.40 ± 0.38 and 0.39 ± 0.36 nmol min-1 m-3, respectively), indicating the significant influence of combustion-related sources on OP. This study provides new insights into the effects of PM2.5 compositions and sources on OP by applying IML models.

Distributions of PAHs, NPAHs, OPAHs, BrPAHs, and ClPAHs in air, bulk deposition, soil, and water in the Shandong Peninsula, China: Urban-rural gradient, interface exchange, and long-range transport.

A systematic study of the movement of PAHs (Polycyclic aromatic hydrocarbons) and their derivatives through air, soil, and water is key to understanding the exchange and transport mechanisms of these pollutants in the environment and for ultimately improving environmental quality. PAHs and their derivatives, such as nitrated PAHs (NPAHs), oxygenated PAHs (OPAHs), brominated PAHs (BrPAHs) and chlorinated PAHs (ClPAHs), were analyzed in air, bulk deposition, soil, and water samples collected from urban, rural, field, and background sites on the eastern coast of China. The goal was to investigate and discuss their spatiotemporal variations, exchange fluxes, and transport potential. The concentrations of PAHs and their derivatives in the air and bulk deposition displayed distinct seasonal patterns, with higher concentrations observed during the winter and spring and lower concentrations during the summer and autumn. NPAHs exhibited the opposite trend. Significant urban-rural gradients were observed for most of the PAHs and their derivatives. According to the air-soil fugacity calculations, 2-3 ring PAHs, BrPAHs, and ClPAHs were found to volatilize from the soil into the air, while 4-7 ring PAHs, OPAHs, and NPAHs deposited from the air into the soil. The air-water fugacity of the PAHs and their derivatives indicated that surface water was an important source for the ambient atmosphere in Qingdao. The characteristic travel distances (CTDs) and persistence (Pov) for atmospheric transport were much lower than that for the water samples, which may be due to the longer half-lives of PAHs and their derivatives in water. NPAHs and ClPAHs with long transport distances and strong persistence in water could lead to a significant impact on marine pollution.

Toward Intrinsic Adversarial Robustness Through Probabilistic Training.

Modern deep neural networks have made numerous breakthroughs in real-world applications, yet they remain vulnerable to some imperceptible adversarial perturbations. These tailored perturbations can severely disrupt the inference of current deep learning-based methods and may induce potential security hazards to artificial intelligence applications. So far, adversarial training methods have achieved excellent robustness against various adversarial attacks by involving adversarial examples during the training stage. However, existing methods primarily rely on optimizing injective adversarial examples correspondingly generated from natural examples, ignoring potential adversaries in the adversarial domain. This optimization bias can induce the overfitting of the suboptimal decision boundary, which heavily jeopardizes adversarial robustness. To address this issue, we propose Adversarial Probabilistic Training (APT) to bridge the distribution gap between the natural and adversarial examples via modeling the latent adversarial distribution. Instead of tedious and costly adversary sampling to form the probabilistic domain, we estimate the adversarial distribution parameters in the feature level for efficiency. Moreover, we decouple the distribution alignment based on the adversarial probability model and the original adversarial example. We then devise a novel reweighting mechanism for the distribution alignment by considering the adversarial strength and the domain uncertainty. Extensive experiments demonstrate the superiority of our adversarial probabilistic training method against various types of adversarial attacks in different datasets and scenarios.

Spatial Distribution, Sources, Air-Soil Exchange, and Health Risks of Parent PAHs and Derivative-Alkylated PAHs in Different Functional Areas of an Oilfield Area in the Yellow River Delta, North China.

The knowledge of the spatial distribution, sources, and air-soil exchange of polycyclic aromatic compounds (PACs) in an oilfield area is essential to the development of effective control practices of PAC pollution. In this study, 48 passive air samples and 24 soil samples were collected during 2018-2019 in seven functional areas (e.g., urban, oil field, suburban, industrial, agricultural, near pump units, and background) in the Yellow River Delta (YRD) where the Shengli Oilfield is located, and 18 parent polycyclic aromatic hydrocarbons (PAHs) and five alkylated-PAHs (APAHs) were analyzed from all the air and soil samples. The ΣPAHs in the air and soil ranged from 2.26 to 135.83 ng/m3 and 33.96 to 408.94 ng/g, while the ΣAPAHs in the atmosphere and soil ranged from 0.04 to 16.31 ng/m3 and 6.39 to 211.86 ng/g, respectively. There was a downward trend of atmospheric ΣPAH concentrations with increasing the distance from the urban area, while both ΣPAH and ΣAPAH concentrations in the soil decreased with distance from the oilfield area. PMF analyses show that for atmospheric PACs, coal/biomass combustion was the main contributor in urban, suburban, and agricultural areas, while crude production and processing source contributes more in the industrial and oilfield area. For PACs in soil, densely populated areas (industrial, urban, and suburban) are more affected by traffic sources, while oilfield and near-pump unit areas are under the impact of oil spills. The fugacity fraction (ff) results indicated that the soil generally emitted low-molecular-weight PAHs and APAHs and act as a sink for high-molecular-weight PAHs. The incremental lifetime cancer risk (ILCR) of Σ(PAH+APAH) in both the air and soil, were below the threshold (≤10-6) set by the US EPA.

Pose Guided Person Image Generation via Dual-task Correlation and Affinity Learning.

Pose Guided Person Image Generation (PGPIG) is the task of transforming a person's image from the source pose to a target pose. Existing PGPIG methods often tend to learn an end-to-end transformation between the source image and the target image, but do not seriously consider two issues: 1) the PGPIG is an ill-posed problem, and 2) the texture mapping requires effective supervision. In order to alleviate these two challenges, we propose a novel method by incorporating Dual-task Pose Transformer Network and Texture Affinity learning mechanism (DPTN-TA). To assist the ill-posed source-to-target task learning, DPTN-TA introduces an auxiliary task, i.e., source-to-source task, by a Siamese structure and further explores the dual-task correlation. Specifically, the correlation is built by the proposed Pose Transformer Module (PTM), which can adaptively capture the fine-grained mapping between sources and targets and can promote the source texture transmission to enhance the details of the generated images. Moreover, we propose a novel texture affinity loss to better supervise the learning of texture mapping. In this way, the network is able to learn complex spatial transformations effectively. Extensive experiments show that our DPTN-TA can produce perceptually realistic person images under significant pose changes. Furthermore, our DPTN-TA is not limited to processing human bodies but can be flexibly extended to view synthesis of other objects, i.e., faces and chairs, outperforming the state-of-the-arts in terms of both LPIPS and FID. Our code is available at: https://github.com/PangzeCheung/Dual-task-Pose-Transformer-Network.

Learning Shadow Removal From Unpaired Samples via Reciprocal Learning.

We focus on addressing the problem of shadow removal for an image, and attempt to make a weakly supervised learning model that does not depend on the pixelwise-paired training samples, but only uses the samples with image-level labels that indicate whether an image contains shadow or not. To this end, we propose a deep reciprocal learning model that interactively optimizes the shadow remover and the shadow detector to improve the overall capability of the model. On the one hand, shadow removal is modeled as an optimization problem with a latent variable of the detected shadow mask. On the other hand, a shadow detector can be trained using the prior from the shadow remover. A self-paced learning strategy is employed to avoid fitting to intermediate noisy annotation during the interactive optimization. Furthermore, a color-maintenance loss and a shadow-attention discriminator are both designed to facilitate model optimization. Extensive experiments on the pairwise ISTD dataset, SRD dataset, and unpaired USR dataset demonstrate the superiority of the proposed deep reciprocal model.

Learning Weak Semantics by Feature Graph for Attribute-Based Person Search.

Attribute-based person search aims to find the target person from the gallery images based on the given query text. It often plays an important role in surveillance systems when visual information is not reliable, such as identifying a criminal from a few witnesses. Although recent works have made great progress, most of them neglect the attribute labeling problems that exist in the current datasets. Moreover, these problems also increase the risk of non-alignment between attribute texts and visual images, leading to large semantic gaps. To address these issues, in this paper, we propose Weak Semantic Embeddings (WSEs), which can modify the data distribution of the original attribute texts and thus improve the representability of attribute features. We also introduce feature graphs to learn more collaborative and calibrated information. Furthermore, the relationship modeled by our feature graphs between all semantic embeddings can reduce the semantic gap in text-to-image retrieval. Extensive evaluations on three challenging benchmarks - PETA, Market-1501 Attribute, and PA100K, demonstrate the effectiveness of the proposed WSEs, and our method outperforms existing state-of-the-art methods.

Representational Geometries Reveal Differential Effects of Response Correlations on Population Codes in Neurophysiology and Functional Magnetic Resonance Imaging.

Two sensory neurons usually display trial-by-trial spike-count correlations given the repeated representations of a stimulus. The effects of such response correlations on population-level sensory coding have been the focal contention in computational neuroscience over the past few years. In the meantime, multivariate pattern analysis (MVPA) has become the leading analysis approach in functional magnetic resonance imaging (fMRI), but the effects of response correlations among voxel populations remain underexplored. Here, instead of conventional MVPA analysis, we calculate linear Fisher information of population responses in human visual cortex (five males, one female) and hypothetically remove response correlations between voxels. We found that voxelwise response correlations generally enhance stimulus information, a result standing in stark contrast to the detrimental effects of response correlations reported in empirical neurophysiological studies. By voxel-encoding modeling, we further show that these two seemingly opposite effects actually can coexist within the primate visual system. Furthermore, we use principal component analysis to decompose stimulus information in population responses onto different principal dimensions in a high-dimensional representational space. Interestingly, response correlations simultaneously reduce and enhance information on higher- and lower-variance principal dimensions, respectively. The relative strength of the two antagonistic effects within the same computational framework produces the apparent discrepancy in the effects of response correlations in neuronal and voxel populations. Our results suggest that multivariate fMRI data contain rich statistical structures that are directly related to sensory information representation, and the general computational framework to analyze neuronal and voxel population responses can be applied in many types of neural measurements.SIGNIFICANCE STATEMENT Despite the vast research interest in the effect of spike-count noise correlations on population codes in neurophysiology, it remains unclear how the response correlations between voxels influence MVPA in human imaging. We used an information-theoretic approach and showed that unlike the detrimental effects of response correlations reported in neurophysiology, voxelwise response correlations generally improve sensory coding. We conducted a series of in-depth analyses and demonstrated that neuronal and voxel response correlations can coexist within the visual system and share some common computational mechanisms. These results shed new light on how the population codes of sensory information can be evaluated via different neural measurements.

Vulnerability and fraud: evidence from the COVID-19 pandemic.

This study examines consumer fraud at the onset of the COVID-19 pandemic and provides novel evidence for the opportunity model of predatory victimization. Scammers have taken advantage of the COVID-19 pandemic shock to exploit victims who are already vulnerable and suffering. The number of fraud cases has greatly increased as COVID-19 spread across the U.S., consistent with the vulnerable-to-become-victimization hypothesis based on the opportunity model of predatory victimization. A Google Trends analysis shows that the increase in fraud and scams is attributable to victims' increased vulnerability rather than to their awareness of fraud and increased motivation to report scams. An improvement in financial literacy is associated with the reduction of finance-related fraud and scams. Finally, we provide important policy implications to protect people from fraud victimization.

A Novel Detachable, Reusable, and Versatile Acoustic Tweezer Manipulation Platform for Biochemical Analysis and Detection Systems.

Multifunctional, integrated, and reusable operating platforms are highly sought after in biochemical analysis and detection systems. In this study, we demonstrated a novel detachable, reusable acoustic tweezer manipulation platform that is flexible and versatile. The free interchangeability of different detachable microchannel devices on the acoustic tweezer platform was achieved by adding a waveguide layer (glass) and a coupling layer (polydimethylsiloxane (PDMS) polymer film). We designed and demonstrated the detachable multifunctional acoustic tweezer platform with three cell manipulation capabilities. In Demo I, the detachable acoustic tweezer platform is demonstrated to have the capability for parallel processing and enrichment of the sample. In Demo II, the detachable acoustic tweezer platform with capability for precise cell alignment is demonstrated. In Demo III, it was demonstrated that the detachable acoustic tweezer platform has the capability for the separation and purification of cells. Through experiments, our acoustic tweezer platform has good acoustic retention ability, reusability, and stability. More capabilities can be expanded in the future. It provides a simple, economical, and multifunctional reusable operating platform solution for biochemical analysis and detection systems.

Evaluation of Online Andrology Medical Services in Central Regions of China During the COVID-19 Pandemic: A Cross-Sectional Data Analysis Study.

To provide an overview of the current situation, challenges, and trends in online medical services from the perspective of andrology and promote the development of online medical services. Users of the Learning Alliance of Urology, who mainly worked in central regions of China, were invited to complete the questionnaire that included information on the participants and their institutions and their involvement in and concerns for online medical services. We received 875 complete responses. The percentage of online andrology patients at most institutions was less than 30%. The most common services were online appointment registration (92.7%) and online payment (81.8%). Online chat consultation (77.7%) was the most common form of consultation. Only 1 in 5 of the institutions had constructed their Internet hospital. Factors related to the percentage of online andrology patients included specialized andrology clinics and wards, sufficient time for doctors to provide online services, more diversified services, and online clinic training. The biggest challenge for online medical services was diagnosis and treatment safety. It is essential to raise awareness of online medical services for hospitals and patients and strengthen standardized management and training of online medical services, especially applicable to central regions of China. However, online medical services cannot wholly replace offline services due to insufficient diagnosis and treatment.

[Pollution Characteristics and Source Analysis of Atmospheric PM2.5-bound Polycyclic Aromatic Hydrocarbons in a Port Area].

In order to explore the pollution characteristics and potential sources of polycyclic aromatic hydrocarbons (PAHs) in the polluted air of a port area, PM2.5 samples (n=59) were collected from Qingdao Port for four seasons from August 2018 to May 2019. The seasonal variation and composition characteristics of PM2.5-bound PAHs were analyzed, the influence of meteorological factors on PAH concentrations was explored using correlation analysis, and the potential sources were analyzed using positive definite matrix factorization and potential source contribution function models. The results showed that the total mean concentration of PAHs was (8.11±12.31) ng·m-3, which was higher in autumn and winter than that in spring and summer. The seasonal molecular compositions of PAHs were similar, dominated by 4-5 ring PAHs (75.43%). Fluoranthene, benzo[e]pyrene, benzo[a]anthracene, phenanthrene, pyrene, and chrysene were the dominant species of PAHs in the study area, which are similar to the major compounds in ship exhaust. Correlation analysis showed that PAH concentrations were significantly negatively correlated with temperature and relative humidity and significantly positively correlated with atmospheric pressure and wind direction and had a poor correlation with wind speed. PMF analysis extracted six contribution factors, and the results indicated that Qingdao Port was mainly influenced by shipping emissions (28.83%), followed by vehicle emissions (20.49%), as well as crude oil volatilization (13.47%). Summer had the greatest impact on shipping emissions. The PSCF results suggested that Beijing-Tianjin-Hebei, Bohai Rim, and northern Shandong were the main source regions for long-range transport.

Computational Photon Counting Using Multithreshold Peak Detection for Fast Fluorescence Lifetime Imaging Microscopy.

Time-resolved photon counting methods have a finite bandwidth that restricts the acquisition speed of techniques like fluorescence lifetime imaging microscopy (FLIM). To enable faster imaging, computational methods can be employed to count photons when the output of a detector is directly digitized at a high sampling rate. Here, we present computational photon counting using a hybrid photodetector in conjunction with multithreshold peak detection to count instances where one or more photons arrive at the detector within the detector response time. This method can be used to distinguish up to five photon counts per digitized point, whereas previous demonstrations of computational photon counting on data acquired with photomultiplier tubes have only counted one photon at a time. We demonstrate in both freely moving C. elegans and a human breast cancer cell line undergoing apoptosis that this novel multithreshold peak detection method can accurately characterize the intensity and fluorescence lifetime of samples producing photon rates up to 223%, higher than previously demonstrated photon counting FLIM systems.