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Photodynamic therapy (PDT) is a clinically approved, minimally invasive therapeutic technique that can induce the regression of targeted lesions via generating excess cytotoxic reactive oxygen species. However, due to the limited penetration depth of visible excitation light and the intrinsic hypoxia microenvironment of solid tumors, the efficacy of PDT in the treatment of cancer, especially deep-seated or large tumors, is unsatisfactory. Herein, we developed an efficient in vivo PDT system based on a nanomaterial, dihydrolipoic acid coated gold nanocluster (AuNC@DHLA), that combined the advantages of large penetration depth in tissue, extremely high two-photon (TP) absorption cross section (σ 2 ∼ 10 6 GM), efficient ROS generation, a type I photochemical mechanism, and negligible in vivo toxicity. With AuNC@DHLA as the photosensitizer, highly efficient in vivo TP-PDT has been achieved.

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MotivationOxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals.ResultsHere we propose a deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83 to 97%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection.Availability and implementationThe software can be accessed freely at: https://github.com/lykaust15/DeepSimulator.Supplementary information Supplementary data are available at Bioinformatics online.

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MotivationSuper-resolution fluorescence microscopy with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures.ResultsHere, we propose a novel deep learning guided Bayesian inference (DLBI) approach, for the time-series analysis of high-density fluorescent images. Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image. In particular, our method contains three main components. The first one is a simulator that takes a high-resolution image as the input, and simulates time-series low-resolution fluorescent images based on experimentally calibrated parameters, which provides supervised training data to the deep learning model. The second one is a multi-scale deep learning module to capture both spatial information in each input low-resolution image as well as temporal information among the time-series images. And the third one is a Bayesian inference module that takes the image from the deep learning module as the initial localization of fluorophores and removes artifacts by statistical inference. Comprehensive experimental results on both real and simulated datasets demonstrate that our method provides more accurate and realistic local patch and large-field reconstruction than the state-of-the-art method, the 3B analysis, while our method is more than two orders of magnitude faster.Availability and implementationThe main program is available at https://github.com/lykaust15/DLBISupplementary information Supplementary data are available at Bioinformatics online.

Molecular imaging techniques based on surface-enhanced Raman scattering (SERS) face a lack of reproducibility and reliability, thus hampering its practical application. Flower-like gold nanoparticles have strong SERS enhancement performance due to having plenty of hot-spots on their surfaces, and this enhancement is not dependent on the aggregation of the particles. These features make this kind of particle an ideal SERS substrate to improve the reproducibility in SERS imaging. Here, the SERS properties of individual flower-like gold nanoparticles are systematically investigated. The measurements reveal that the enhancement of a single gold nanoparticle is independent of the polarization of the excitation laser with an enhancement factor as high as 10(8) . After capping with Raman signal molecules and folic acid, the gold nanoflowers show strong Raman signal in the living cells, excellent targeting properties, and a high signal-to-noise ratio for SERS imaging.

Background Anaplasma spp. are tick-transmitted bacteria that infect a wide variety of wild and domestic animals. These pathogens exhibit a high degree of biological diversity, broad geographical distribution, and represent a serious threat to veterinary and public health worldwide.ResultsA novel Anaplasma species was identified in Haemaphysalis qinghaiensis (Ixodidae) in northwestern China and was molecularly characterized by comparison of 16S rRNA, gltA, and groEL gene sequences. Of the 414 samples tested, 24 (5.8%) were positive for this Anaplasma species. On the basis of the 16S rRNA gene, this organism has been found to be closely related to and exhibit the highest sequence similarity with A. capra (99.8–99.9%) that was identified in goats and humans in northern China, but was distinct from other known Anaplasma species. Sequence analysis of the gltA and groEL genes revealed that this Anaplasma species was distinct from A. capra considering the lower sequence identity (88.6–88.7% for gltA and 90.6–91.0% for groEL) and a divergent phylogenetic position. Therefore, we described this Anaplasma species as A. capra-like bacteria.ConclusionsThe present study reports a potential novel Anaplasma species closely related to A. capra in H. qinghaiensis in northwestern China. The zoonotic potential of A. capra-like bacteria needs to be further determined.