Joint probabilistic shaping and pre-equalization for hollow-core fiber transmission using end-to-end learning.

In this Letter, we propose a novel, to the best of our knowledge, end-to-end (E2E) learning scheme leveraging a time-frequency decoupling network (TFDnet) for joint probabilistic shaping (PS) and pre-equalization in hollow-core fiber (HCF)-based wavelength division multiplexing (WDM) systems. The TFDnet emulator effectively models HCF transmission channels by decoupling signal impairments into high-frequency, linear, and nonlinear distortions. Furthermore, a TFDnet emulator-based E2E strategy for joint PS and pre-equalization is presented with the aim of compensating the signal impairment for the HCF-based WDM systems.

Delivery of Marker-Free DNA to Plant Genome by the Transgenic Selection-Associated Fragment Elimination (T-SAFE) System.

The presence of a selection marker in transgenic plants has raised public concerns regarding health safety. We have developed a CRISPR/Cas9-based DNA delivery system termed transgenic selection-associated fragment elimination (T-SAFE). The T-SAFE system comprises four cassettes: the selection marker, CRISPR/Cas9, spacer-plus-protospacer adjacent motif (SP), and the cargo.

SNR enhanced OAM mode division multiplexing with in-band noise modulation based on cascade delta-sigma modulation.

Due to mode coupling, a high signal-to-noise ratio (SNR) is required in orbital angular momentum (OAM) modular division multiplexing (MDM) systems to improve transmission performance. In this paper, a cascade delta-sigma modulation (CDSM) scheme is proposed for OAM-MDM intensity modulation and direct detection (IM/DD) transmission. Different from the traditional DSM (TDSM) scheme, the scheme is divided into signal modulation and in-band noise modulation, in which the in-band noise modulation is used to further decrease the quantization noise generated in the signal modulation.

Physics-driven untrained neural network for vortex beam compensation in adaptive optics aided underwater wireless optical communications.

Orbital angular momentum (OAM) multiplexing is emerging as a critical technique for achieving high data capacity in underwater wireless optical communications (UWOC). Nonetheless, wavefront distortions induced by underwater turbulence compromise the orthogonality of OAM modes. In this paper, we introduce a physics-driven untrained learning approach for adaptive optics that operates independently of extensive amplitude datasets.

Inhibition of mPFC norepinephrine improved chronic post-thoracotomy pain in adult rats.

Background: Chronic post-thoracotomy pain (CPTP) is characterized by high incidence, long duration, and severity of pain. Medial prefrontal cortex (mPFC) is a brain region closely associated with chronic pain, and norepinephrine is involved in pain regulation. But the role of mPFC norepinephrine in CPTP and its possible mechanism is unclear.

Comprehensive analysis identifies a lactylation-related signature for predicting prognosis and guiding therapies in colon adenocarcinoma.

Purpose: This study aimed to identify a lactylation-related gene signature for predicting prognosis and guiding therapies in colon adenocarcinoma (COAD). We seek to address the challenges in COAD prognostication due to tumor heterogeneity and variable treatment responses.

Methods: The study employed integrative bioinformatics analyses on multi-omics data from public databases, including gene expression profiles, clinical data, and lactylation-related genes (LRGs).

GLR36 Mutation of Casuarina equisetifoli Is Associated With a Decreased JA Response to Insect Feeding by Lymantria xylina.

Lymantria xylina is the most important defoliator, damaging the effective coastal windbreak tree species Casuarina equisetifolia. However, the underlying genetic mechanisms through which C. equisetifolia responds to L.

Dynamic perturbation mitigation via polarization difference neural network for high-fidelity ring core fiber image transmission.

Ring core fibers (RCFs) offer unique advantages in fiber image transmission, as their weakly-coupled orbital angular momentum mode groups result in high resolution images. However, severe image distortion is still exhibited during fiber transmission when subjected to strong disturbances. Here, we present a novel approach with a differential neural network, namely the polarization speckle differential imaging (PSDI) method, to significantly enhance both the robustness and image resolution of RCF-based imaging systems.

All-optical image transmission through a dynamically perturbed multimode fiber and a ring-core fiber using diffractive deep neural networks.

In this study, we present an all-optical image reconstruction technique leveraging a diffractive deep neural network (D2NN) within a ring-core fiber (RCF) architecture. Orbital angular momentum (OAM) modes are employed to facilitate imaging transmission. We experimentally validate the efficacy of our approach for complex field diffractive image reconstruction through a multimode fiber (MMF) and RCF at a 1550 nm operating wavelength.

End-to-end learning of joint noise shaping and probabilistic shaping for OAM mode division multiplexing transmission.

Intensity modulation direct detection (IM/DD) orbital angular momentum (OAM) mode division multiplexing (MDM) technology can greatly expand the capacity of a communication system, which is a promising solution for the next generation of high-speed passive optical networks (PONs). However, there are serious obstacles such as mode coupling, device nonlinear impairment, and quantization noise in an IM/DD OAM-MDM system with a low-resolution digital-to-analog converter (DAC). In this Letter, we propose a novel, to the best of our knowledge, end-to-end (E2E) learning scheme based on a double residual feature decoupling network (DRFDnet) emulator with joint probabilistic shaping (PS) and noise shaping (NS) for the OAM-MDM IM/DD transmission.

A rare giant atrial septal cardiac blood cyst in an adult: a case report.

Background: Cardiac blood cysts are exceedingly rare cardiac tumours usually found on cardiac valves in infants. We report and discuss a rare unique case wherein a giant atrial septal cardiac blood cyst was found in an adult.

Case Summary: A 59-year-old Chinese lady with history of hypertension, hyperlipidemia and transient ischaemic attack presented with atypical chest pain.

Nomogram Model for Cardiac Surgery-Associated Acute Kidney Injury Based on Clinical Characteristics Combined with Plasma suPAR.

Objective: Analyze risk factors for cardiac surgery-associated acute kidney injury (CSA-AKI) in adults and establish a nomogram model for CSA-AKI based on plasma soluble urokinase-type plasminogen activator receptor (suPAR) and clinical characteristics.

Methods: In a study of 170 patients undergoing cardiac surgery with cardiopulmonary bypass, enzyme-linked immunosorbent assay (ELISA) measured plasma suPAR levels. Multivariable logistic regression analysis identified risk factors associated with CSA-AKI.

A highly sensitive and selective colorimetric aptasensor for detecting sulfadiazine in river waters based on gold nanoparticles synthesized from discarded Longan seed extract.

Gold nanoparticles (AuNPs) were extensively employed for in-situ detection sulfadiazine (SDZ) residues, yet current synthesis methods suffer from complex procedures, reagent pollution of the environment, and low particle quality. This study presents a novel synthesis method using discarded longan seed extract as a reducing agent to synthesized high-quality AuNPs, and then can be used for in-situ SDZ detection. Response surface methodology (RSM) was employed to optimize synthesis parameters, which resulted in five optimal combinations that enhanced the flexibility of synthesis.

Characterization of prostatic cancer lesion and gleason grade using a continuous-time random-walk diffusion model at high b-values.

Background: Distinguishing between prostatic cancer (PCa) and chronic prostatitis (CP) is sometimes challenging, and Gleason grading is strongly associated with prognosis in PCa. The continuous-time random-walk diffusion (CTRW) model has shown potential in distinguishing between PCa and CP as well as predicting Gleason grading.

Purpose: This study aimed to quantify the CTRW parameters (α, β & Dm) and apparent diffusion coefficient (ADC) of PCa and CP tissues; and then assess the diagnostic value of CTRW and ADC parameters in differentiating CP from PCa and low-grade PCa from high-grade PCa lesions.

Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases.

Rationale And Objectives: To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases.

Materials And Methods: In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio.

Equalization system of low differential mode delay few-mode fibers based on the neural network MIMO algorithm.

In recent years, with the development of information networks, higher requirements for transmission capacity have been recommended. Yet, at the same time, the capacity of single-mode fiber is rapidly approaching the theoretical limit. The multidimensional multiplexing technique is an effective way to solve this problem.

A Deep Learning Based Platform for Remote Sensing Images Change Detection Integrating Crowdsourcing and Active Learning.

Remote sensing images change detection technology has become a popular tool for monitoring the change type, area, and distribution of land cover, including cultivated land, forest land, photovoltaic, roads, and buildings. However, traditional methods which rely on pre-annotation and on-site verification are time-consuming and challenging to meet timeliness requirements. With the emergence of artificial intelligence, this paper proposes an automatic change detection model and a crowdsourcing collaborative framework.

DACNN-aided nonlinear equalizer for a probabilistic shaping coherent optical communication system.

The probabilistic shaping (PS) technique is a key technology for fiber optic communication systems to further approach the Shannon limit. To solve the problem that nonlinear equalizers are ineffective for probabilistic shaping optical communication systems with non-uniform distribution, a distribution alignment convolutional neural network (DACNN)-aided nonlinear equalizer is proposed. The approach calibrates the equalizer using the probabilistic shaping prior distribution, which reduces the training complexity and improves the performance of the equalizer simultaneously.

OAM mode-division multiplexing IM/DD transmission at 4.32 Tbit/s with a low-complexity adaptive-network-based fuzzy inference system nonlinear equalizer.

Stochastic nonlinear impairment is the primary factor that limits the transmission performance of high-speed orbital angular momentum (OAM) mode-division multiplexing (MDM) optical fiber communication systems. This Letter presents a low-complexity adaptive-network-based fuzzy inference system (LANFIS) nonlinear equalizer for OAM-MDM intensity-modulation direct-detection (IM/DD) transmission with three OAM modes and 15 wavelength division multiplex (WDM) channels. The LANFIS equalizer could adjust the probability distribution functions (PDFs) of the distorted pulse amplitude modulation (PAM) symbols to fit the statistical characteristics of the WDM-OAM-MDM transmission channel.