Fluorescence fluctuation-based super-resolution microscopy is a cost-effective and widely applicable super-resolution microscopic technology, which has broad applications in observing subcellular structures and monitoring their kinetic processes. However, it is highly demanded to systematically study the reconstruction quality of various fluctuation-based superresolution algorithms under different fluorescence temporal fluctuations. In this paper, firstly, the performances in the image quality of multiple algorithms under different conditions are quantitatively analyzed and compared by employing various evaluation metrics such as Resolution Scaled Pearson’s coefficient (RSP), Resolution Scaled Error (RSE), Signal-to-Noise Ratios (SNRs), Relative error of strength (K), and Resolution (R). Then, a comprehensive evaluation factor (CEF) was defined by combining 5 parameters for the evaluation of all algorithms, and the advantages and applicable conditions of various algorithms are summarized and analyzed. Furthermore, this paper establishes a general model using a multi-layer perceptron (MLP) to accurately predict the most suitable super-resolution algorithms under different conditions. In addition, a software platform for multi-algorithm fluorescence fluctuation super-resolution nanoscopy was developed, which can realize the generation of fluorescence fluctuation signals, synchronize multiple super-resolution algorithms, and the evaluation of reconstructed images. The results show that high-quality super-resolution images can be obtained by increasing image frames together with enhancing the fluorescence fluctuation signals. The model of the multilayer perceptron performs well with high output accuracy (>95%) after multiple iterations of training and exhibits good prediction capability that can reduce additional experiments and improve experimental efficiency. These studies will facilitate the implementation of fluctuation-based super-resolution techniques for the research of subcellular organelle investigation under various fluorescent labeling conditions.

Fluorescence fluctuation-based super-resolution microscopy (FF-SRM) is an economical and widely applicable technique that significantly enhances the spatial resolution of fluorescence imaging by capitalizing on fluorescence intermittency. However, each variant of FF-SRM imaging has inherent limitations. This study proposes a super-resolution reconstruction strategy (synSRM) by synergizing multiple variants of the FF-SRM approach to address the limitations and achieve high-quality and high-resolution imaging. The simulation and experimental results demonstrate that, compared to images reconstructed using single FF-SRM algorithms, by selecting suitable synSRM routes according to various imaging conditions, further improvements of the spatial resolution and image reconstruction quality can be obtained for super-resolution fluorescence imaging. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Vulvovaginal candidiasis (VVC) is an inflammatory disease primarily infected by Candida albicans. The condition has good short-term treatment effects, high recurrence, and seriously affects the quality of life of women. Metabolomics has been applied to research a variety of inflammatory diseases. In the present study, the vaginal metabolic profiles of VVC patients and healthy populations (Cnotrol (CTL)) were explored by a non-targeted metabolomics approach. In total, 211 differential metabolites were identified, with the VVC group having 128 over-expressed and 83 under-expressed metabolites compared with healthy individuals. Functional analysis showed that these metabolites were mainly involved in amino acid metabolism and lipid metabolism. In addition, network software analysis indicated that the differential metabolites were associated with mitogen-activated protein kinase (MAPK) signaling and NF-κB signaling. Further molecular docking suggested that linoleic acid can bind to the acyl-CoA synthetase 1 (ACSL1) protein, which has been shown to be associated with multiple inflammatory diseases and is an upstream regulator of the MAPK and NF-κB signaling pathways that mediate inflammation. Therefore, our preliminary analysis results suggest that VVC has a unique metabolic profile. Linoleic acid, a significantly elevated unsaturated fatty acid in the VVC group, may promote VVC development through the ACSL1/MAPK and ACSL1/NF-κB signaling pathways. This study's findings contribute to further exploring the mechanism of VVC infection and providing new perspectives for the treatment of Candida albicans vaginal infection.

With the high renewable energy(RE) penetration, the power system is facing more and more stability issues. Among them, the power angle stability(PAS) and frequency stability(FS) have close association with the power generation status. A generation optimization planning method integrating PAS and FS is proposed in this paper, which incorporates the power angle stable cut constraint obtained through PSAT based on EEAC, and frequency stable cut constraint obtained through Simulink based on equivalent frequency response model, achieving the stability requirement while ensuring economic efficiency. The approach collaborates the construction of thermal power units, wind power, solar power and energy storage using the column and constraint generation algorithm. The advantages of the proposed approach are validated through a provincial power system in China.

In the new power system, the viability of planning scheme depends largely on the suitability of the wind and photovoltaic (WP) output scenario. A scenario reconstruction model for wind and photovoltaic power considering the spatio-temporal correlation and credibility is proposed in this paper. The probability density of scenario about each moment is obtained by kernel density estimation. The Copula function is used to obtain the WP output correlation and generate the reconstructed scenario in each season. The credibility of WP output from historical data and reconstructed data are calculated respectively. It can be obtained from the credibility comparison results that the scenario reconstruction model can fully reflect the real situation.

(1) Background: Although the application of modern diagnostic tests and vaccination against human papillomavirus has markedly reduced the incidence and mortality of early cervical cancer, advanced cervical cancer still has a high death rate worldwide. Glycosylation is closely associated with tumor invasion, metabolism, and the immune response. This study explored the relationship among glycosylation-related genes, the immune microenvironment, and the prognosis of cervical cancer. (2) Methods and results: Clinical information and glycosylation-related genes of cervical cancer patients were downloaded from the TCGA database and the Molecular Signatures Database. Patients in the training cohort were split into two subgroups using consensus clustering. A better prognosis was observed to be associated with a high immune score, level, and status using ESTIMATE, CIBERSORT, and ssGSEA analyses. The differentially expressed genes were revealed to be enriched in proteoglycans in cancer and the cytokine-cytokine receptor interaction, as well as in the PI3K/AKT and the Hippo signaling pathways according to functional analyses, including GO, KEGG, and PPI. The prognostic risk model generated using the univariate Cox regression analysis, LASSO algorithm and multivariate Cox regression analyses, and prognostic nomogram successfully predicted the survival and prognosis of cervical cancer patients. (3) Conclusions: Glycosylation-related genes are correlated with the immune microenvironment of cervical cancer and show promising clinical prediction value.

An optical fiber design automation platform based on deep learning is built. It supports the fast, accurate and flexible design of weak-coupling few mode fiber, with arbitrary dispersion single mode fiber and so on.

In this work, we demonstrate an active learning method for the optimized design of a few-mode fiber (FMF) with equalized zero dispersion between four modes, which can be used for short-reach mode-division-multiplexed (MDM) optical communication without multi-input-multi-output (MIMO) processing and chromatic dispersion compensation (CDC). To obtain the desired FMF, a multi-parameter design of a complex fiber structure is needed, which is usually very difficult, inaccurate, and time-consuming. The proposed active learning can utilize fewer data than the neural network to achieve improved prediction performance by selecting more valuable data. By balancing zero dispersion, equalized dispersion, and manufacturing feasibility, structure parameters of the four-ring step-index FMF supporting four modes are predicted by the active-learning-based inverse design. The standard deviation of four-mode dispersion of the designed fiber is 0.016. The total dataset is significantly reduced to 400 by using active learning and equalized zero dispersion is obtained. The equalized zero dispersion performance is characterized by using an optical parametric amplification (OPA) modal which is highly sensitive to dispersion. The broad OPA gains with high pump power and low amplification cross talk indicate that the designed FMF has low dispersion near to zero, low nonlinearity, and weak coupling for all four modes, which is highly suitable for high-speed MIMO-less and CDC-less MDM optical communications.

A four-mode fiber with equalized zero dispersion is inversely designed by using neural-network for supporting short reach MIMOless and CDCless MDM optical communication. Only 0.017 standard deviation of dispersion at zero-dispersion-wavelength of 1550nm is achieved.

To overcome the capacity crunch of optical communications based on the traditional single-mode fiber (SMF), different modes in a few-mode fiber (FMF) can be employed for mode division multiplexing (MDM). MDM can also be extended to photonic integration for obtaining improved density and efficiency, as well as interconnection capacity. Therefore, MDM becomes the most promising method for maintaining the trend of "Moore's law" in photonic integration and optical fiber transmission. In this tutorial, we provide a review of MDM works and cutting-edge progresses from photonic integration to optical fiber transmission, including our recent works of MDM low-noise amplification, FMF fiber design, MDM Si photonic devices, and so on. Research and application challenges of MDM for optical communications regarding long-haul trans-mission and short reach interconnection are discussed as well. The content is expected to be of important value for both academic researchers and industrial engineers during the development of next-generation optical communication systems, from photonic chips to fiber links.

In this work, mode-division multiplexing (MDM) phase-sensitive amplification (PSA) for all-optical mode selective and mode equalized phase regeneration is presented and investigated. MDM PSA relies on reasonable phase-matching conditions, which makes intramode four-wave mixing in the fiber much stronger than that of cross-mode. It enables multimode signals to interact mainly with the same spatial mode pumps and idlers. Thus, MDM signals can obtain synchronous phase regeneration and avoid cross talk with different mode signals. To achieve this, we designed an appropriate few-mode fiber by an inverse design method based on a neural network for obtaining the desired phase mismatch and low modal dispersion. The results show that effective phase regeneration can be achieved for the degraded 40 Gbit/s binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals. For BPSK signals, error vector magnitudes (EVMs) of the regenerated signal on two modes are reduced from − 8.013 d B and − 8.068 d B to − 24.867 d B , and − 26.090 d B , respectively. For QPSK signals, the EVMs are reduced from − 16.767 d B and − 16.583 d B to − 24.867 d B and − 24.822 d B , respectively.

Few-mode fiber (FMF) supporting many modes with weak-coupling is highly desired in mode division multiplexing (MDM) systems. The multi-parameter design of FMF becomes comparably difficult, inaccurate and time-consuming when it comes for complex fiber structures and many high order modes. In this work, we demonstrate a machine learning method using neural network to inversely design the desired FMF based on multiple-ring structure. By using the minimum index difference between adjacent modes as the weak-coupling optimization aim, we realize the inverse design of 4-ring step-index FMFs for supporting 4, 6 and 10 -mode operation, and 6-ring step-index FMF for supporting 20-mode operation. This method provides high-accuracy, high-efficiency and low-complexity for fast and reusable design of optical fibers, including particularly weak-coupling FMF in this work. It can be widely extended to a lot of fibers and has great potential for instantaneous applications in the optical fiber industry. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Few-mode fiber (FMF) supporting many modes with weak-coupling is highly desired in mode division multiplexing (MDM) systems. The multi-parameter design of FMF becomes comparably difficult, inaccurate and time-consuming when it comes for complex fiber structures and many high order modes. In this work, we demonstrate a machine learning method using neural network to inversely design the desired FMF based on multiple-ring structure. By using the minimum index difference between adjacent modes as the weak-coupling optimization aim, we realize the inverse design of 4-ring step-index FMFs for supporting 4, 6 and 10 -mode operation, and 6-ring step-index FMF for supporting 20-mode operation. This method provides high-accuracy, high-efficiency and low-complexity for fast and reusable design of optical fibers, including particularly weak-coupling FMF in this work. It can be widely extended to a lot of fibers and has great potential for instantaneous applications in the optical fiber industry.