Low complexity exponential pruning learned digital back-propagation method for fiber nonlinearity mitigation.

Learned digital back-propagation (LDBP) is emerging as a promising solution to mitigate the nonlinear fiber distortions that limit the capacity of optical communications. However, the computational complexity of LDBP, which is affected by dispersion compensation, will increase significantly as optical communications move toward higher baud rates and longer transmission distances. Herein, we propose what we believe to be a novel method called exponential pruning LDBP (EP-LDBP), which is achieved by pruning the redundant LDBP taps with adjustable parameters.

KIF9 Ameliorates Neuropathology and Cognitive Dysfunction by Promoting Macroautophagy in a Mouse Model of Alzheimer's Disease.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting the elderly. The imbalance of protein production and degradation processes leads to the accumulation of misfolded and abnormally aggregated amyloid-beta (Aβ) in the extracellular space and forms senile plaques, which constitute one of the most critical pathological hallmarks of AD. KIF9, a member of the kinesin protein superfamily, mediates the anterograde transport of intracellular cargo along microtubules.

Real-time fully digital control scheme for pulse coherent beam combining.

A fully digital control scheme for non-polarization-maintaining (non-PM) nanosecond pulse coherent beam combining (CBC) is proposed, where digital locking of optical coherence by single-detector electronic-frequency tagging (LOCSET) for active phase control and stochastic parallel gradient descent (SPGD) for active polarization control is proposed. The fully digital control scheme is integrated on a real-time field-programmable gate array (FPGA) empowered hardware platform and then experimentally validated in a four-channel all-fiber non-fully polarization-maintaining nanosecond pulse CBC system. Consequently, the system can be fully locked in 9.

Learnable digital signal processing: a new benchmark of linearity compensation for optical fiber communications.

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).

High-fidelity sub-petabit-per-second self-homodyne fronthaul using broadband electro-optic combs.

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.

Enhanced mutual information neural estimators for optical fiber communication.

The accurate estimation of mutual information (MI) plays a vital role in understanding channel capacity and optimizing the performance of optical communications. While MI computations for the additive white Gaussian noise (AWGN) channel are well-established, they fall short when dealing with the challenges posed by nonlinear optical fiber channels due to an unknown channel model. For the first time, to our knowledge, this Letter introduces a mutual information neural estimator (MINE) for MI estimation in optical fiber communications.

mediates memory decline by targeting MKP-1 to reduce surface GluA1-containing AMPA receptors in a mouse model of Alzheimer's disease.

Alzheimer's disease (AD) is a leading cause of dementia in the elderly. Mitogen-activated protein kinase phosphatase 1 (MKP-1) plays a neuroprotective role in AD. However, the molecular mechanisms underlying the effects of MKP-1 on AD have not been extensively studied.

PCSK6 exacerbates Alzheimer's disease pathogenesis by promoting MT5-MMP maturation.

Proprotein convertase subtilisin/kexin type 6 (PCSK6) is a calcium-dependent serine proteinase that regulates the proteolytic activity of various precursor proteins and facilitates protein maturation. Dysregulation of PCSK6 expression or function has been implicated in several pathological processes including nervous system diseases. However, whether and how PCSK6 is involved in the pathogenesis of Alzheimer's disease (AD) remains unclear.

Fangchinoline alleviates cognitive impairments through enhancing autophagy and mitigating oxidative stress in Alzheimer's disease models.

Alzheimer's disease (AD) is a debilitating, progressive, neurodegenerative disorder characterized by the deposition of amyloid-β (Aβ) peptides and subsequent oxidative stress, resulting in a cascade of cytotoxic effects. Fangchinoline (Fan), a bisbenzylisoquinoline alkaloid isolated from traditional Chinese herb Stephania tetrandra S. Moorec, has been reported to possess multiple potent biological activities, including anti-inflammatory and antioxidant properties.

Data-driven inverse design of mode-locked fiber lasers.

The diverse applications of mode-locked fiber lasers (MLFLs) raise various demands on the output of the laser, including the pulse duration, energy, and shape. Simulation is an excellent method to guide the design and construction of an MLFL for on-demand laser output. Traditional simulation of an MLFL uses the split-step Fourier method (SSFM) to solve the nonlinear Schrödinger (NLS) equation, which suffers from high computational complexity.

Signal-to-noise ratio degradation analysis for optoelectronic feedback-based chaotic optical communication systems.

Chaotic optical communication encrypts transmitted signals through physical noise; this ensures high security while causing a certain decrease in the signal-to-noise ratio (SNR). Thus, it is necessary to analyze the SNR degradation of decrypted signals after chaotic encryption and the minimum requirements for the SNR of the fiber channel to meet the required bit error rate (BER) performance. Accordingly, an SNR model of decrypted signals for optoelectronic feedback-based chaotic optical communication systems is proposed.

Inhibiting NLRP3 inflammasome signaling pathway promotes neurological recovery following hypoxic-ischemic brain damage by increasing p97-mediated surface GluA1-containing AMPA receptors.

Background: The nucleotide-binding oligomeric domain (NOD)-like receptor protein 3 (NLRP3) inflammasome is believed to be a key mediator of neuroinflammation and subsequent secondary brain injury induced by ischemic stroke. However, the role and underlying mechanism of the NLRP3 inflammasome in neonates with hypoxic-ischemic encephalopathy (HIE) are still unclear.

Methods: The protein expressions of the NLRP3 inflammasome including NLRP3, cysteinyl aspartate specific proteinase-1 (caspase-1) and interleukin-1β (IL-1β), the α-amino-3-hydroxy-5-methyl-4-isoxazole-propionicacid receptor (AMPAR) subunit, and the ATPase valosin-containing protein (VCP/p97), were determined by Western blotting.

Deep learning-based vortex decomposition and switching based on fiber vector eigenmodes.

Structured optical fields, such as cylindrical vector (CV) and orbital angular momentum (OAM) modes, have attracted considerable attention due to their polarization singularities and helical phase wavefront structure. However, one of the most critical challenges is still the intelligent generation or precise control of these modes. Here, we demonstrate the first simulation and experimental realization of decomposing the CV and OAM modes by reconstructing the multi-view images of projected intensity distribution.

Digital pre-distortion using a Gauss-Newton-based direct learning architecture for coherent optical transmitters.

Digital pre-distortion (DPD) is a powerful technique to mitigate transmitter nonlinear distortion in optical transmissions. In this Letter, the identification of DPD coefficients based on the direct learning architecture (DLA) using the Gauss-Newton (GN) method is applied in optical communications for the first time. To the best of our knowledge, this is the first time that the DLA has been realized without training an auxiliary neural network to mitigate optical transmitter nonlinear distortion.

Low-complexity Volterra-inspired neural network equalizer in 100-G band-limited IMDD PON system.

One of the most promising solutions for 100 Gb/s line-rate passive optical networks (PONs) is intensity modulation and direct detection (IMDD) technology together with a digital signal processing- (DSP-) based equalizer for its advantages of system simplicity, cost-effectiveness, and energy-efficiency. However, due to restricted hardware resources, the effective neural network (NN) equalizer and Volterra nonlinear equalizer (VNLE) have the drawback of high implementation complexity. In this paper, we incorporate an NN with the physical principles of a VNLE to construct a white-box low-complexity Volterra-inspired neural network (VINN) equalizer.

Spectral broadening scheme for suppressing SBS effects based on time-domain optimized chirp-like signals.

We propose a novel (to our knowledge) driving scheme to suppress the stimulated Brillouin scattering (SBS) effect in master oscillator power amplification (MOPA) systems based on an external high-order phase modulation. Since seed sources with the linear chirp can uniformly broaden the SBS gain spectrum with a high SBS threshold, a chirp-like signal was designed by applying further editing and processing to the piecewise parabolic signal. Compared with the traditional piecewise parabolic signal, the chirp-like signal has similar linear chirp characteristics and can reduce the driving power and sampling rate requirements, enabling more efficient spectral spreading.

Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method.

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schrödinger equation (NLSE) has long been regarded as extremely time-consuming, especially for designing and optimizing experiments. The recurrent neural network (RNN) has been implemented as an accurate intensity prediction tool with reduced complexity and good generalization capability.

Modeling of a multi-parameter chaotic optoelectronic oscillator based on the Fourier neural operator.

A model construction scheme of chaotic optoelectronic oscillator (OEO) based on the Fourier neural operator (FNO) is proposed. Different from the conventional methods, we learn the nonlinear dynamics of OEO (actual components) in a data-driven way, expecting to obtain a multi-parameter OEO model for generating chaotic carrier with high-efficiency and low-cost. FNO is a deep learning architecture which utilizes neural network as a parameter structure to learn the trajectory of the family of equations from training data.

Coherent digital-analog radio-over-fiber (DA-RoF) system with a CPRI-equivalent data rate beyond 1 Tb/s for fronthaul.

We propose and experimentally demonstrate a coherent digital-analog radio-over-fiber (DA-RoF) system and achieve the transmission of Tb/s common public radio interface (CPRI)-equivalent data rate for fronthaul. The proposed coherent DA-RoF system includes DA-RoF modulation, demodulation and DA-RoF compatible coherent digital signal processing (DSP) blocks. A theoretical analysis of the DA-RoF scheme together with parameter optimization is accomplished as well.

Asymmetric point-to-multipoint coherent architecture with a frequency aliasing recovery algorithm for cost-constraint short-reach access networks.

An asymmetric point-to-multipoint (PTMP) coherent architecture combined with a frequency aliasing recovery (FAR) algorithm is proposed for cost-constraint short-reach access networks. In this architecture, the uplink transmitters are simplified significantly with the uplink dual-polarization four-level pulse amplitude modulation (DP-PAM4) and downlink DP quadrature phase shift keying (DP-QPSK) asymmetric transmission design. Digital to analog converters (DACs) and radio frequency (RF) drivers are reduced by half, and in-phase and quadrature modulators (IQMs) are replaced by Mach-Zehnder modulators (MZMs), saving four MZ interferometers (MZIs).

Optical filtering impairment monitoring based on model fusion for optical networks.

Reconfigurable optical add-and-drop multiplexer (ROADM) is a key element in optical networks. As several ROADMs are cascaded over long paths, the penalty induced by ROADM has become non-negligible due to the tight optical filtering. In this case, for efficient and reliable network planning and operation, accurate monitoring of optical filtering penalty is very important.