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Mode-locked fiber lasers based on nonlinear polarization evolution can generate femtosecond pulses with different pulse widths and rich spectral distributions for versatile applications through polarization tuning. However, a precise and repeatable location of a specific pulsation regime is extremely challenging. Here, by using fast spectral analysis based on a time-stretched dispersion Fourier transform as the spectral discrimination criterion, along with an intelligent polarization search algorithm, for the first time, we achieved real-time control of the spectral width and shape of mode-locked femtosecond pulses; the spectral width can be tuned from 10 to 40 nm with a resolution of ~1.47 nm, and the spectral shape can be programmed to be hyperbolic secant or triangular. Furthermore, we reveal the complex, repeatable transition dynamics of the spectrum broadening of femtosecond pulses, including five middle phases, which provides deep insight into ultrashort pulse formation that cannot be observed with traditional mode-locked lasers.Lasers: Fine control over very short pulsesA method providing greater control over very short duration light pulses generated by systems called mode-locked fiber lasers (MLFLs) will enhance the use of the light in such diverse applications as atomic clocks, radars, optical computing, measuring systems and astronomy. Researchers in China led by Lilin Yi at Shanghai Jiao Tong University developed apparatus and software algorithms allowing automatic ‘intelligent control’ over the generation of MLFL light pulses. The system can manipulate key aspects of the frequency range and composition of the pulses – technically their ‘spectral width’ and ‘spectral shape’ – more effectively than previously possible. The procedure also yields new technical insights into the factors determining the nature of the generated light pulses. The researchers believe their low-cost and portable system will find widespread application in research and industry.

Conventional approaches to femtosecond (fs) pulse characterization depend on nonlinearities without exception, thereby impeding their utilization in measuring weak fs pulses. Their reliance on iterative phase retrieval or spectrometer-based readout further prevents single-shot full-field characterization over high-repetition-rate fs pulse trains. Here, we present linear spectral shearing interferometry (LSSI) based intelligent single-shot full-field characterization (ISFC) towards high-repetition-rate fs pulse trains, where both intensity and phase of fs pulses are reconstructed in single shot from temporal interferograms through a well-trained fully-connected neural network. The spectral shear in LSSI is created completely by linear effects enabling measurement of weak fs pulses. We validate the proposed method on a megahertz fs pulse train with picojoule-level energy. Moreover, the switching dynamics of a programmable spectral filter are successfully resolved with the proposed method at a megahertz frame rate. We anticipate LSSI-based ISFC to be a particularly powerful tool for characterizing weak ultraviolet fs pulses, and even attosecond pulses with high repetition rates. Approaches to femtosecond pulse characterization have inherent limitations due to their reliance on nonlinearity and iterative retrieval algorithms. Here, the authors implement AI-assisted linear spectral shearing interferometry, showing single-shot characterization of femtosecond pulse trains with high repetition rate and low energies.

Posttranslational modification (PTM) of the amyloid precursor protein (APP) plays a critical role in Alzheimer's disease (AD). Recent evidence reveals that lactylation modification, as a novel PTM, is implicated in the occurrence and development of AD. However, whether and how APP lactylation contributes to both the pathogenesis and cognitive function in AD remains unknown. Here, we observed a reduction in APP lactylation in AD patients and AD model mice and cells. Proteomic mass spectrometry analysis further identified lysine 612 (APP-K612la) as a crucial site for APP lactylation, influencing APP amyloidogenic processing. A lactyl-mimicking mutant (APPK612T) reduced amyloid-β peptide (Aβ) generation and slowed down cognitive deficits in vivo. Mechanistically, APPK612T appeared to facilitate APP trafficking and metabolism. However, lactylated APP entering the endosome inhibited its binding to BACE1, suppressing subsequent cleavage. Instead, it promoted protein interaction between APP and CD2-associated protein (CD2AP), thereby accelerating the endosomal-lysosomal degradation pathway of APP. In the APP23/PS45 double-transgenic mouse model of AD, APP-Kla was susceptible to L-lactate regulation, which reduced Aβ pathology and repaired spatial learning and memory deficits. Thus, these findings suggest that targeting APP lactylation may be a promising therapeutic strategy for AD in humans.

Polarization control in nonlinear polarization rotation based mode-locked fiber lasers is a long-term challenge. Suffering from the polarization drifts induced by environmental disturbances, nonlinear polarization rotation based mode-locked fiber lasers is difficult in continuously operating under the desired pulsation regime thereby substantially hindering their utilizations. The appearance of automatic mode-locking techniques brings the light in addressing this challenge. Combining with various algorithms and electrical polarization control, automatic mode-locking techniques resolve the dilemma of nonlinear polarization rotation based mode-locked fiber lasers. We review the research progress of automatic mode-locking techniques in detail. Furthermore, we comment on the perspectives and potential applications of automatic mode-locking techniques.

The surge in interest regarding the next generation of optical fiber transmission has stimulated the development of digital signal processing (DSP) schemes that are highly cost-effective with both high performance and low complexity. As benchmarks for nonlinear compensation methods, however, traditional DSP designed with block-by-block modules for linear compensations, could exhibit residual linear effects after compensation, limiting the nonlinear compensation performance. Here we propose a high-efficient design thought for DSP based on the learnable perspectivity, called learnable DSP (LDSP). LDSP reuses the traditional DSP modules, regarding the whole DSP as a deep learning framework and optimizing the DSP parameters adaptively based on backpropagation algorithm from a global scale. This method not only establishes new standards in linear DSP performance but also serves as a critical benchmark for nonlinear DSP designs. In comparison to traditional DSP with hyperparameter optimization, a notable enhancement of approximately 1.21 dB in the Q factor for 400 Gb/s signal after 1600 km fiber transmission is experimentally demonstrated by combining LDSP and perturbation-based nonlinear compensation algorithm. Benefiting from the learnable model, LDSP can learn the best configuration adaptively with low complexity, reducing dependence on initial parameters. The proposed approach implements a symbol-rate DSP with a small bit error rate (BER) cost in exchange for a 48% complexity reduction compared to the conventional 2 samples/symbol processing. We believe that LDSP represents a new and highly efficient paradigm for DSP design, which is poised to attract considerable attention across various domains of optical communications.

With the exponential growth in data density and user ends of wireless networks, fronthaul is tasked with supporting aggregate bandwidths exceeding thousands of gigahertz while accommodating high-order modulation formats. However, it must address the bandwidth and noise limitations imposed by optical links and devices in a cost-efficient manner. Here we demonstrate a high-fidelity fronthaul system enabled by self-homodyne digital-analog radio-over-fiber superchannels, using a broadband electro-optic comb and uncoupled multicore fiber. This self-homodyne superchannel architecture not only offers capacity boosting but also supports carrier-recovery-free reception. Our approach achieves a record-breaking 15,000 GHz aggregated wireless bandwidth, corresponding to a 0.879 Pb/s common public radio interface (CPRI) equivalent data rate. Higher-order formats up to 1,048,576 quadrature-amplitude-modulated (QAM) are showcased at a 100 Tb/s class data rate. Furthermore, we employ a packaged on-chip electro-optic comb as the sole optical source to reduce the cost, supporting a data rate of 100.5 Tb/s with the 1024-QAM format. These demonstrations propel fronthaul into the era of Pb/s-level capacity and exhibit the promising potential of integrated-photonics implementation, pushing the boundaries to new heights in terms of capacity, fidelity, and cost. Here the authors propose a self-homodyne fronthaul architecture, utilizing DA-RoF super channels and multicore fiber, paving the way for the Pb/s era in fronthaul transmission, enabling ultra-highspeed Internet access. The remarkable data speeds reaching 0.879 Pb/s and the 256-QAM format make it possible for 150,000 5G channels to be accessed simultaneously.

Alzheimer's disease (AD) is a common neurodegenerative disease in the elderly. Its pathological features include: A lot of misfolding and abnormal aggregation of amyloid protein (Aβ); Autophagy disorder, oxidative stress, neuroinflammation, abnormal phosphorylated tau protein and synaptic dysfunction. Modern pharmacological studies have found that Paisinhin A (PA) has beneficial effects on the prevention and treatment of central nervous system diseases. This study aims to explore the role and mechanism of PA in AD through autophagy pathway, and lay a scientific foundation for the development of clinical prevention and treatment strategies for AD. N2A cells were treated with different concentrations of PA. Cell viability was detected by CCK-8 method. Western blotting detected the expression levels of proteins related to amyloid production, autophagy pathway, and phosphorylated Tau expression levels. Autophagy flow was detected by transfecting Lc3 double fluorescent plasmid. After Aβ was injected into the hippocampus of WT mice and PA was injected intraperitoneally, the learning and memory ability of WT mice were tested by new object recognition, y maze and water maze. The oxidative stress level was detected by the kit. The levels of inflammatory factors were detected by RT-qpcr. The viability of N2A cells was not affected at different concentrations of PA, but PS1 was significantly decreased at 40μM. PA can obviously improve the accumulation of autophagy in AD, and to some extent save the autophagy inhibition of CQ. Behavioral studies have shown that PA can also improve learning and memory impairments caused by Aβ injections. In addition, experiments, PA can also improve oxidative stress levels, inflammation levels and salvage dysfunctions of synapses. PA also reduces the levels of total and phosphorylated Tau in N2A . Our study provides the first evidence that PA improves learning and memory in Aβ-induced AD mice. This effect appears to be mediated by PA by promoting autophagy and reducing oxidative stress. It was also found that PA may have a role in regulating inflammation, improving abnormally phosphorylated tau, and salvaging damaged synaptic function, providing valuable insights into potential applications in the treatment and prevention of AD.

ELK1 is a member of the E-twenty-six transcription factor family and is usually activated by phosphorylation at Ser383 and Ser389 by extracellular signal-regulated kinase 1/2 (ERK1/2). Dysregulation of ERK1/2 is involved in Alzheimer’s disease (AD)-related neuropathogenesis and cognitive impairments. However, the role of ELK1 in AD pathogenesis remains unclear. Here we report that the expression of ELK1 was significantly increased in the brain tissues of patients with AD and AD model mice. The genetic knockdown of ELK1 or inhibition of its phosphorylation by an interfering peptide (TAT-DEF-ELK1 (TDE)) reduced amyloidogenic processing of APP by targeting PS1, consequently inhibiting Aβ generation and alleviating synaptic and memory impairments in APP23/PS45 double-transgenic AD model mice. In addition, we further found that ELK1 regulated the expression of PS1 by competitively inhibiting the interaction between PS1 and its E3 ubiquitin ligase synoviolin (SYVN1), thereby inhibiting the SYVN1-mediated ubiquitination and degradation of PS1. Our results demonstrate that ELK1 aberrantly increases in AD and genetic or pharmacological inhibition of ELK1 can alleviate AD-related pathology and memory impairments by enhancing the SYVN1-mediated PS1 ubiquitination and degradation, indicating that ELK1 may be a novel target for AD treatment. ELK1 elevation linked to Alzheimer’s disease athology Alzheimer’s disease is a leading cause of memory impairment in older adults. Researchers found that ELK1 levels are higher in patients with Alzheimer’s disease and animal models. Through genetic and drug methods to suppress ELK1 activity, they observed reduced accumulation of pathogenic proteins and improved memory in mice. This study employs a comprehensive approach involving human and murine brain tissues analysis, as well as cell cultures. The investigation specifically examined the regulatory effects of ELK1 on PS1, a protein implicated in the production of neurotoxic amyloid plaques. They discovered that ELK1 inhibits PS1 degradation, leading to more plaque formation. By suppressing ELK1 activity, they observed a decrease in plaque formation and improvements in memory tests for mice. This suggests that targeting ELK1 could be a potential treatment strategy for Alzheimer’s disease. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

Parkinson’s disease (PD) is a neurodegenerative disease characterized by the loss of dopaminergic neurons in the substantia nigra and diminished dopamine content in the striatum. Recent reports show that 7,8-dihydroxyflavone (DHF), a TrkB agonist, attenuates the α-synuclein deposition and ameliorates motor deficits. However, the underlying mechanism is unclear. In this study, we investigated whether autophagy is involved in the clearance of α-synuclein and the signaling pathway through which DHF exerts therapeutic effects. We found that the administration of DHF (5 mg/kg/day, i.p.) prevented the loss of dopaminergic neurons and improved motor functions in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model of PD, whereas these protective effects of DHF were completely blocked by autophagy inhibitor chloroquine (CQ). Further in vitro studies showed that autophagy was inhibited in N2A cells treated with 1-methyl-4-phenylpyridinium (MPP+), as reflected by a significant decrease in the expressions of autophagy marker proteins (Beclin1 and LC3II) and an increase in the expression of autophagic flux marker p62. DHF restored the impaired autophagy to control level in MPP+-treated N2A cells by inhibiting the ERK-LKB1-AMPK signaling pathway. Taken together, these results demonstrate that DHF exerts therapeutic effects in MPTP/MPP+-induced neurotoxicity by inhibiting the ERK-LKB1-AMPK signaling pathway and subsequently improving impaired autophagy.

With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.