Dissecting embryonic and extraembryonic lineage crosstalk with stem cell co-culture.

Embryogenesis necessitates harmonious coordination between embryonic and extraembryonic tissues. Although stem cells of both embryonic and extraembryonic origins have been generated, they are grown in different culture conditions. In this study, utilizing a unified culture condition that activates the FGF, TGF-ß, and WNT pathways, we have successfully derived embryonic stem cells (FTW-ESCs), extraembryonic endoderm stem cells (FTW-XENs), and trophoblast stem cells (FTW-TSCs) from the three foundational tissues of mouse and cynomolgus monkey (Macaca fascicularis) blastocysts. This approach facilitates the co-culture of embryonic and extraembryonic stem cells, revealing a growth inhibition effect exerted by extraembryonic endoderm cells on pluripotent cells, partially through extracellular matrix signaling. Additionally, our cross-species analysis identified both shared and unique transcription factors and pathways regulating FTW-XENs. The embryonic and extraembryonic stem cell co-culture strategy offers promising avenues for developing more faithful embryo models and devising more developmentally pertinent differentiation protocols.

Kinome-wide siRNA screen identifies a DCLK2-TBK1 oncogenic signaling axis in clear cell renal cell carcinoma.

TANK-binding kinase 1 (TBK1) is a potential therapeutic target in multiple cancers, including clear cell renal cell carcinoma (ccRCC). However, targeting TBK1 in clinical practice is challenging. One approach to overcome this challenge would be to identify an upstream TBK1 regulator that could be targeted therapeutically in cancer specifically. In this study, we perform a kinome-wide small interfering RNA (siRNA) screen and identify doublecortin-like kinase 2 (DCLK2) as a TBK1 regulator in ccRCC. DCLK2 binds to and directly phosphorylates TBK1 on Ser172. Depletion of DCLK2 inhibits anchorage-independent colony growth and kidney tumorigenesis in orthotopic xenograft models. Conversely, overexpression of DCLK2203, a short isoform that predominates in ccRCC, promotes ccRCC cell growth and tumorigenesis in vivo. Mechanistically, DCLK2203 elicits its oncogenic signaling via TBK1 phosphorylation and activation. Taken together, these results suggest that DCLK2 is a TBK1 activator and potential therapeutic target for ccRCC.

Modeling genotype-protein interaction and correlation for Alzheimer's disease: a multi-omics imaging genetics study.

Integrating and analyzing multiple omics data sets, including genomics, proteomics and radiomics, can significantly advance researchers' comprehensive understanding of Alzheimer's disease (AD). However, current methodologies primarily focus on the main effects of genetic variation and protein, overlooking non-additive effects such as genotype-protein interaction (GPI) and correlation patterns in brain imaging genetics studies. Importantly, these non-additive effects could contribute to intermediate imaging phenotypes, finally leading to disease occurrence. In general, the interaction between genetic variations and proteins, and their correlations are two distinct biological effects, and thus disentangling the two effects for heritable imaging phenotypes is of great interest and need. Unfortunately, this issue has been largely unexploited. In this paper, to fill this gap, we propose $\textbf{M}$ulti-$\textbf{T}$ask $\textbf{G}$enotype-$\textbf{P}$rotein $\textbf{I}$nteraction and $\textbf{C}$orrelation disentangling method ($\textbf{MT-GPIC}$) to identify GPI and extract correlation patterns between them. To ensure stability and interpretability, we use novel and off-the-shelf penalties to identify meaningful genetic risk factors, as well as exploit the interconnectedness of different brain regions. Additionally, since computing GPI poses a high computational burden, we develop a fast optimization strategy for solving MT-GPIC, which is guaranteed to converge. Experimental results on the Alzheimer's Disease Neuroimaging Initiative data set show that MT-GPIC achieves higher correlation coefficients and classification accuracy than state-of-the-art methods. Moreover, our approach could effectively identify interpretable phenotype-related GPI and correlation patterns in high-dimensional omics data sets. These findings not only enhance the diagnostic accuracy but also contribute valuable insights into the underlying pathogenic mechanisms of AD.

The innate immunity protein IFITM3 modulates γ-secretase in Alzheimer's disease.

Innate immunity is associated with Alzheimer's disease1, but the influence of immune activation on the production of amyloid-ß is unknown2,3. Here we identify interferon-induced transmembrane protein 3 (IFITM3) as a γ-secretase modulatory protein, and establish a mechanism by which inflammation affects the generation of amyloid-ß. Inflammatory cytokines induce the expression of IFITM3 in neurons and astrocytes, which binds to γ-secretase and upregulates its activity, thereby increasing the production of amyloid-ß. The expression of IFITM3 is increased with ageing and in mouse models that express familial Alzheimer's disease genes. Furthermore, knockout of IFITM3 reduces γ-secretase activity and the formation of amyloid plaques in a transgenic mouse model (5xFAD) of early amyloid deposition. IFITM3 protein is upregulated in tissue samples from a subset of patients with late-onset Alzheimer's disease that exhibit higher γ-secretase activity. The amount of IFITM3 in the γ-secretase complex has a strong and positive correlation with γ-secretase activity in samples from patients with late-onset Alzheimer's disease. These findings reveal a mechanism in which γ-secretase is modulated by neuroinflammation via IFITM3 and the risk of Alzheimer's disease is thereby increased.

Mapping developmental regionalization and patterns of cortical surface area from 29 post-menstrual weeks to 2 years of age.

Surface area of the human cerebral cortex expands extremely dynamically and regionally heterogeneously from the third trimester of pregnancy to 2 y of age, reflecting the spatial heterogeneity of the underlying microstructural and functional development of the cerebral cortex. However, little is known about the developmental patterns and regionalization of cortical surface area during this critical stage, due to the lack of high-quality imaging data and accurate computational tools for pediatric brain MRI data. To fill this critical knowledge gap, by leveraging 1,037 high-quality MRI scans with the age between 29 post-menstrual weeks and 24 mo from 735 pediatric subjects in two complementary datasets, i.e., the Baby Connectome Project (BCP) and the developing Human Connectome Project (dHCP), and state-of-the-art dedicated image-processing tools, we unprecedentedly parcellate the cerebral cortex into a set of distinct subdivisions purely according to the developmental patterns of the cortical surface. Our discovered developmentally distinct subdivisions correspond well to structurally and functionally meaningful regions and reveal spatially contiguous, hierarchical, and bilaterally symmetric patterns of early cortical surface expansion. We also show that high-order association subdivisions, where cortical folds emerge later during prenatal stages, undergo more dramatic cortical surface expansion during infancy, compared with the central regions, especially the sensorimotor and insula cortices, thus forming a distinct central-pole division in early cortical surface expansion. These results provide an important reference for exploring and understanding dynamic early brain development in health and disease.

HBV integrations reshaping genomic structures promote hepatocellular carcinoma.

OBJECTIVE: Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC), mostly characterised by HBV integrations, is prevalent worldwide. Previous HBV studies mainly focused on a few hotspot integrations. However, the oncogenic role of the other HBV integrations remains unclear. This study aimed to elucidate HBV integration-induced tumourigenesis further. DESIGN: Here, we illuminated the genomic structures encompassing HBV integrations in 124 HCCs across ages using whole genome sequencing and Nanopore long reads. We classified a repertoire of integration patterns featured by complex genomic rearrangement. We also conducted a clustered regularly interspaced short palindromic repeat (CRISPR)-based gain-of-function genetic screen in mouse hepatocytes. We individually activated each candidate gene in the mouse model to uncover HBV integration-mediated oncogenic aberration that elicits tumourigenesis in mice. RESULTS: These HBV-mediated rearrangements are significantly enriched in a bridge-fusion-bridge pattern and interchromosomal translocations, and frequently led to a wide range of aberrations including driver copy number variations in chr 4q, 5p (TERT), 6q, 8p, 16q, 9p (CDKN2A/B), 17p (TP53) and 13q (RB1), and particularly, ultra-early amplifications in chr8q. Integrated HBV frequently contains complex structures correlated with the translocation distance. Paired breakpoints within each integration event usually exhibit different microhomology, likely mediated by different DNA repair mechanisms. HBV-mediated rearrangements significantly correlated with young age, higher HBV DNA level and TP53 mutations but were less prevalent in the patients subjected to prior antiviral therapies. Finally, we recapitulated the TONSL and TMEM65 amplification in chr8q led by HBV integration using CRISPR/Cas9 editing and demonstrated their tumourigenic potentials. CONCLUSION: HBV integrations extensively reshape genomic structures and promote hepatocarcinogenesis (graphical abstract), which may occur early in a patient's life.

Mapping Genetic Topography of Cortical Thickness and Surface Area in Neonatal Brains.

Adult twin neuroimaging studies have revealed that cortical thickness (CT) and surface area (SA) are differentially influenced by genetic information, leading to their spatially distinct genetic patterning and topography. However, the postnatal origins of the genetic topography of CT and SA remain unclear, given the dramatic cortical development from neonates to adults. To fill this critical gap, this study unprecedentedly explored how genetic information differentially regulates the spatial topography of CT and SA in the neonatal brain by leveraging brain magnetic resonance (MR) images from 202 twin neonates with minimal influence by the complicated postnatal environmental factors. We capitalized on infant-dedicated computational tools and a data-driven spectral clustering method to parcellate the cerebral cortex into a set of distinct regions purely according to the genetic correlation of cortical vertices in terms of CT and SA, respectively, and accordingly created the first genetically informed cortical parcellation maps of neonatal brains. Both genetic parcellation maps exhibit bilaterally symmetric and hierarchical patterns, but distinct spatial layouts. For CT, regions with closer genetic relationships demonstrate an anterior-posterior (A-P) division, while for SA, regions with greater genetic proximity are typically within the same lobe. Certain genetically informed regions exhibit strong similarities between neonates and adults, with the most striking similarities in the medial surface in terms of SA, despite their overall substantial differences in genetic parcellation maps. These results greatly advance our understanding of the development of genetic influences on the spatial patterning of cortical morphology.SIGNIFICANCE STATEMENT Genetic influences on cortical thickness (CT) and surface area (SA) are complex and could evolve throughout the lifespan. However, studies revealing distinct genetic topography of CT and SA have been limited to adults. Using brain structural magnetic resonance (MR) images of twins, we unprecedentedly discovered the distinct genetically-informed parcellation maps of CT and SA in neonatal brains, respectively. Each genetic parcellation map comprises a distinct spatial layout of cortical regions, where vertices within the same region share high genetic correlation. These genetic parcellation maps of CT and SA of neonates largely differ from those of adults, despite their highly remarkable similarities in the medial cortex of SA. These discoveries provide important insights into the genetic organization of the early cerebral cortex development.

eLIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity.

Spatial segmentation is an essential processing method for image analysis aiming to identify the characteristic suborgans or microregions from mass spectrometry imaging (MSI) data, which is critical for understanding the spatial heterogeneity of biological information and function and the underlying molecular signatures. Due to the intrinsic characteristics of MSI data including spectral nonlinearity, high-dimensionality, and large data size, the common segmentation methods lack the capability for capturing the accurate microregions associated with biological functions. Here we proposed an ensemble learning-based spatial segmentation strategy, named eLIMS, that combines a randomized unified manifold approximation and projection (r-UMAP) dimensionality reduction module for extracting significant features and an ensemble pixel clustering module for aggregating the clustering maps from r-UMAP. Three MSI datasets are used to evaluate the performance of eLIMS, including mouse fetus, human adenocarcinoma, and mouse brain. Experimental results demonstrate that the proposed method has potential in partitioning the heterogeneous tissues into several subregions associated with anatomical structure, i.e., the suborgans of the brain region in mouse fetus data are identified as dorsal pallium, midbrain, and brainstem. Furthermore, it effectively discovers critical microregions related to physiological and pathological variations offering new insight into metabolic heterogeneity.

KLF13 promotes VSMCs phenotypic dedifferentiation by directly binding to the SM22α promoter.

Krüppel-like factor 13 (KLF13), a zinc finger transcription factor, is considered as a potential regulator of cardiomyocyte differentiation and proliferation during heart morphogenesis. However, its precise role in the dedifferentiation of vascular smooth muscle cells (VSMCs) during atherosclerosis and neointimal formation after injury remains poorly understood. In this study, we investigated the relationship between KLF13 and SM22α expression in normal and atherosclerotic plaques by bioanalysis, and observed a significant increase in KLF13 levels in the atherosclerotic plaques of both human patients and ApoE-/- mice. Knockdown of KLF13 was found to ameliorate intimal hyperplasia following carotid artery injury. Furthermore, we discovered that KLF13 directly binds to the SM22α promoter, leading to the phenotypic dedifferentiation of VSMCs. Remarkably, we observed a significant inhibition of platelet-derived growth factor BB-induced VSMCs dedifferentiation, proliferation, and migration when knocked down KLF13 in VSMCs. This inhibitory effect of KLF13 knockdown on VCMC function was, at least in part, mediated by the inactivation of p-AKT signaling in VSMCs. Overall, our findings shed light on a potential therapeutic target for treating atherosclerotic lesions and restenosis after vascular injury.

CRKL dictates anti-PD-1 resistance by mediating tumor-associated neutrophil infiltration in hepatocellular carcinoma.

BACKGROUND & AIMS: The effectiveness of immune checkpoint inhibitor (ICI) therapy for hepatocellular carcinoma (HCC) is limited by treatment resistance. However, the mechanisms underlying immunotherapy resistance remain elusive. We aimed to identify the role of CT10 regulator of kinase-like (CRKL) in resistance to anti-PD-1 therapy in HCC. METHODS: Gene expression in HCC specimens from 10 patients receiving anti-PD-1 therapy was identified by RNA-sequencing. A total of 404 HCC samples from tissue microarrays were analyzed by immunohistochemistry. Transgenic mice (Alb-Cre/Trp53fl/fl) received hydrodynamic tail vein injections of a CRKL-overexpressing vector. Mass cytometry by time of flight was used to profile the proportion and status of different immune cell lineages in the mouse tumor tissues. RESULTS: CRKL was identified as a candidate anti-PD-1-resistance gene using a pooled genetic screen. CRKL overexpression nullifies anti-PD-1 treatment efficacy by mobilizing tumor-associated neutrophils (TANs), which block the infiltration and function of CD8+ T cells. PD-L1+ TANs were found to be an essential subset of TANs that were regulated by CRKL expression and display an immunosuppressive phenotype. Mechanistically, CRKL inhibits APC (adenomatous polyposis coli)-mediated proteasomal degradation of ß-catenin by competitively decreasing Axin1 binding, and thus promotes VEGFα and CXCL1 expression. Using human HCC samples, we verified the positive correlations of CRKL/ß-catenin/VEGFα and CXCL1. Targeting CRKL using CRISPR-Cas9 gene editing (CRKL knockout) or its downstream regulators effectively restored the efficacy of anti-PD-1 therapy in an orthotopic mouse model and a patient-derived organotypic tumor spheroid model. CONCLUSIONS: Activation of the CRKL/ß-catenin/VEGFα and CXCL1 axis is a critical obstacle to successful anti-PD-1 therapy. Therefore, CRKL inhibitors combined with anti-PD-1 could be useful for the treatment of HCC. IMPACT AND IMPLICATIONS: Here, we found that CRKL was overexpressed in anti-PD-1-resistant hepatocellular carcinoma (HCC) and that CRKL upregulation promotes anti-PD-1 resistance in HCC. We identified that upregulation of the CRKL/ß-catenin/VEGFα and CXCL1 axis contributes to anti-PD-1 tolerance by promoting infiltration of tumor-associated neutrophils. These findings support the strategy of bevacizumab-based immune checkpoint inhibitor combination therapy, and CRKL inhibitors combined with anti-PD-1 therapy may be developed for the treatment of HCC.

Circ_0098823 binding with IGF2BP3 regulates DNM1L stability to promote metastasis of hepatocellular carcinoma via mitochondrial fission.

Hepatocellular carcinoma (HCC) is highly metastatic and invasive. CircRNA participates in gene regulation of multiple tumor metastases, but little is known whether it is a bystander or an actual player in HCC metastasis. We aim to explore the molecular mechanisms of novel circRNAs in HCC metastasis. RT-qPCR was used to detect the expression of 13 circRNAs derived by the ERBB3 gene. The function of circ_0098823 and DNM1L in HCC cells were estimated by CCK-8, transwell assays, flow cytometry, electron microscope, and in vivo experiments. RNA binding protein of circ_0098823 was confirmed by RNA pull-down, mass spectrometry, and RNA immunoprecipitation. The expression of DNM1L after IGF2BP3 knockdown was detected by RT-qPCR and western blot. Circ_0098823 was significantly up-regulated both in HCC tissues and HGF induced cell lines. Circ_0098823 overexpression significantly enhanced proliferation, migration, and invasion but decreased apoptosis of HCC cells, particularly promoted mitochondrial fission. Compared with the control group, the tumors in the circ_0098823 knockdown mice were significantly smaller and lighter. Circ_0098823 silencing suppressed DNM1L expression, a key molecule for fission, which enhanced proliferation, migration and invasion, and inhibited apoptosis of HCC cell. IGF2BP3 was a binding protein of circ_0098823. The expression and mRNA stability of DNM1L were down-regulated by IGF2BP3 knockdown. IGF2BP3 knockdown significantly alleviated the excessive migration, invasion and apoptosis of HCC cells caused by circ_0098823 overexpression. This study uncovered a novel circ_0098823 with tumor-promoting effect, and the mechanism by which circ_0098823 participates in HCC progression through IGF2BP3-guided DNM1L. Our study broadens molecular understanding of HCC progression.

DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.

Abundance of Modifications in Mature miRNAs Revealed by LC-MS/MS Method Coupled with a Two-Step Hybridization Purification Strategy.

Accurate detection of endogenous miRNA modifications, such as N6-methyladenosine (m6A), 7-methylguanosine (m7G), and 5-methylcytidine (m5C), poses significant challenges, resulting in considerable uncertainty regarding their presence in mature miRNAs. In this study, we demonstrate for the first time that liquid chromatography coupled with a tandem mass spectrometry (LC-MS/MS) nucleoside analysis method is a practical tool for quantitatively analyzing human miRNA modifications. The newly designed liquid-solid two-step hybridization (LSTH) strategy enhances specificity for miRNA purification, while LC-MS/MS offers robust capability in recognizing modifications and sufficient sensitivity with detection limits ranging from attomoles to low femtomoles. Therefore, it provides a more reliable approach compared to existing techniques for revealing modifications in endogenous miRNAs. With this approach, we characterized m6A, m7G, and m5C modifications in miR-21-5p, Let-7a/e-5p, and miR-10a-5p isolated from cultured cells and observed unexpectedly low abundance (<1% at each site) of these modifications.

Scalable Dual In Situ Synthesis of Polyester Nanocomposites for High-Energy Storage.

Incorporating ultralow loading of nanoparticles into polymers has realized increases in dielectric constant and breakdown strength for excellent energy storage. However, there are still a series of tough issues to be dealt with, such as organic solvent uses, which face enormous challenges in scalable preparation. Here, a new strategy of dual in situ synthesis is proposed, namely polymerization of polyethylene terephthalate (PET) synchronizes with growth of calcium borate nanoparticles, making polyester nanocomposites from monomers directly. Importantly, this route is free of organic solvents and surface modification of nanoparticles, which is readily accessible to scalable synthesis of polyester nanocomposites. Meanwhile, uniform dispersion of as ultralow as 0.1 wt% nanoparticles and intense bonding at interfaces have been observed. Furthermore, the PET-based nanocomposite displays obvious increases in both dielectric constant and breakdown strength as compared to the neat PET. Its maximum discharged energy density reaches 15 J cm-3 at 690 MV m-1 and power density attains 218 MW cm-3 under 150 Ω resistance at 300 MV m-1, which is far superior to the current dielectric polymers that can be produced at large scales. This work presents a scalable, safe, low-cost, and environment-friendly route toward polymer nanocomposites with superior capacitive performance.

Advancements in the study of synaptic plasticity and mitochondrial autophagy relationship.

Synapses serve as the points of communication between neurons, consisting primarily of three components: the presynaptic membrane, synaptic cleft, and postsynaptic membrane. They transmit signals through the release and reception of neurotransmitters. Synaptic plasticity, the ability of synapses to undergo structural and functional changes, is influenced by proteins such as growth-associated proteins, synaptic vesicle proteins, postsynaptic density proteins, and neurotrophic growth factors. Furthermore, maintaining synaptic plasticity consumes more than half of the brain's energy, with a significant portion of this energy originating from ATP generated through mitochondrial energy metabolism. Consequently, the quantity, distribution, transport, and function of mitochondria impact the stability of brain energy metabolism, thereby participating in the regulation of fundamental processes in synaptic plasticity, including neuronal differentiation, neurite outgrowth, synapse formation, and neurotransmitter release. This article provides a comprehensive overview of the proteins associated with presynaptic plasticity, postsynaptic plasticity, and common factors between the two, as well as the relationship between mitochondrial energy metabolism and synaptic plasticity.

Reconfigurable all-optical pattern recognition for PSK and QAM signals in high-speed optoelectronic firewalls.

In the fifth generation fixed networks (F5G) era, full-fiber-connected optical networks support emerging bandwidth-hungry services. However, optical networks are vulnerable to attack by tapping or other methods, which has been paid more and more attention in modern optical infrastructure. Therefore, high-speed optoelectronic firewalls appear as one of the promising technologies to guarantee security. The most significant and challenging component of a high-speed optoelectronic firewall is all-optical pattern recognition, especially for more advanced high-order modulation formats such as phase shift keying (PSK) or quadrature amplitude modulation (QAM) to satisfy efficient enhanced fixed broadband in F5G. In this paper, what we believe to be a novel reconfigurable all-optical pattern recognition system for PSK and QAM signals is proposed with two implementation architectures. The proposed system mainly consists of a generalized XNOR (GXNOR) and a recirculating loop. The two implementation architectures are precisely two realization methods of the GXNOR part. One employs two cascaded IQ Mach-Zehnder modulators and the other is implemented by the four-wave mixing. The numerical simulation results demonstrate that the two implementation architectures can both achieve all-optical pattern recognition for the reconfigurable high-order modulation formats of QPSK, 8PSK, and 16QAM with the recorded baud rate of 260GBaud.

Machine-learning-based optical spectrum feature analysis for DoS attack detection in IP over optical networks.

In this paper, we introduce a novel approach for detecting Denial of Service (DoS) attacks in software-defined IP over optical networks, leveraging machine learning to analyze optical spectrum features. This method employs machine learning to automatically process optical spectrum data, which is indicative of network security status, thereby identifying potential DoS attacks. To validate its effectiveness, we conducted both numerical simulations and experimental trials to collect relevant optical spectrum datasets. We then assessed the performance of three machine learning algorithms XGBoost, LightGBM, and the BP neural network in detecting DoS attacks. Our findings show that all three algorithms demonstrate a detection accuracy exceeding 97%, with the BP neural network achieving the highest accuracy rates of 99.55% and 99.74% in simulations and experiments, respectively. This research not only offers a new avenue for DoS attack detection but also enhances early detection capabilities in the underlying optical network through optical spectrum data analysis.

Convex optimization-based high-speed and security joint optimization scheme in optical access networks.

Data rate and security are essential performance metrics for passive optical networks (PON). However, existing optical access networks lack standardized metrics to evaluate rate and security performance uniformly. This paper introduces a high-speed and security joint optimization scheme for optical access networks using convex optimization. Evaluation metrics for data rate and security performance in PON are established. According to the evaluation metrics, the security optimization objective function Us, high-speed optimization objective function GMI, and high-speed security joint-optimization objective function Hs are established. An optimization problem is formulated to maximize weighted rate and security indicators, factoring in constraints such as maximum power, probability, amplifier capacity, normalized mutual information, and key and frame lengths. An alternating optimization method is applied to iteratively address sub-problems by exploiting successive convex approximations and differences of convex functions. This transforms non-convex sub-problems into convex optimizations. Experimental results highlight notable improvements in objective function values, confirming the effectiveness of the proposed high-speed security optimization algorithm for optical access networks.

Turbulence-resistant free space optical communication via chaotic block-matching and 3D filtering.

In this paper, we propose a chaotic block-matching and three-dimensional (C-BM3D) filtering algorithm to remove the noise and enhance the security in the turbulent channel of free space optical (FSO) communication. We experimentally demonstrate the performance of C-BM3D by comparing it with chaotic non-local means filtering (C-NLM), chaotic Gaussian filtering and chaotic Median filtering based on Log-normal and Gamma-Gamma turbulence models. The results show that the peak signal-to-noise ratios (PSNRs) of C-BM3D in the weak turbulence under Log-normal and Gamma-Gamma models are up to 96.2956 and 93.2853, respectively. The C-BM3D also achieves superior image similarity in Log-normal turbulent channel, with its structural similarity index measures (SSIMs) nearly equal to 1. Additionally, the signal-to-noise ratio (SNR) of C-BM3D ranks the highest, and its bit error rate (BER) improves by at least 15 dB compared to that of the other three algorithms. The experimental results indicate that the C-BM3D can be a good candidate for the next generation of FSO communication in security and turbulence resistance.

Effects of the stress hyperglycemia ratio on long-term mortality in patients with triple-vessel disease and acute coronary syndrome.

AIMS: Risk assessment for triple-vessel disease (TVD) remain challenging. Stress hyperglycemia represents the regulation of glucose metabolism in response to stress, and stress hyperglycemia ratio (SHR) is recently found to reflect true acute hyperglycemic status. This study aimed to evaluate the prognostic value of SHR and its role in risk stratification in TVD patients with acute coronary syndrome (ACS). METHODS: A total of 3812 TVD patients with ACS with available baseline SHR measurement were enrolled from two independent centers. The endpoint was cardiovascular mortality. Cox regression was used to evaluate the association between SHR and cardiovascular mortality. The SYNTAX (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery) II (SSII) was used as the reference model in the model improvement analysis. RESULTS: During a median follow-up of 5.1 years, 219 (5.8%) TVD patients with ACS suffered cardiovascular mortality. TVD patients with ACS with high SHR had an increased risk of cardiovascular mortality after robust adjustment for confounding (high vs. median SHR: adjusted hazard ratio 1.809, 95% confidence interval 1.160-2.822, P = 0.009), which was fitted as a J-shaped pattern. The prognostic value of the SHR was found exclusively among patients with diabetes instead of those without diabetes. Moreover, addition of SHR improved the reclassification abilities of the SSII model for predicting cardiovascular mortality in TVD patients with ACS. CONCLUSIONS: The high level of SHR is associated with the long-term risk of cardiovascular mortality in TVD patients with ACS, and is confirmed to have incremental prediction value beyond standard SSII. Assessment of SHR may help to improve the risk stratification strategy in TVD patients who are under acute stress.