OptiComm-GPT: a GPT-based versatile research assistant for optical fiber communication systems.

With the increasing capacity and complexity of optical fiber communication systems, both academic and industrial requirements for the essential tasks of transmission systems simulation, digital signal processing (DSP) algorithms verification, system performance evaluation, and quality of transmission (QoT) optimization are becoming significantly important. However, due to the intricate and nonlinear nature of optical fiber communication systems, these tasks are generally implemented in a divide-and-conquer manner, which necessitates a profound level of expertise and proficiency in software programming from researchers or engineers. To lower this threshold and facilitate professional research easy-to-start, a GPT-based versatile research assistant named OptiComm-GPT is proposed for optical fiber communication systems, which flexibly and automatically performs system simulation, DSP algorithms verification, performance evaluation, and QoT optimization with only natural language. To enhance OptiComm-GPT's abilities for complex tasks in optical fiber communications and improve the accuracy of generated results, a domain information base containing rich domain knowledge, tools, and data as well as the comprehensive prompt engineering with well-crafted prompt elements, techniques, and examples is established and performs under a LangChain-based framework. The performance of OptiComm-GPT is evaluated in multiple simulation, verification, evaluation, and optimization tasks, and the generated results show that OptiComm-GPT can effectively comprehend the user's intent, accurately extract system parameters from the user's request, and intelligently invoke domain resources to solve these complex tasks simultaneously. Moreover, the statistical results, typical errors, and running time of OptiComm-GPT are also investigated to illustrate its practical reliability, potential limitations, and further improvements.

Real-time demonstration of two-aperture coherent digital combining free-space optical transmission with a real-valued MIMO adaptive equalizer.

Compared with the single-aperture system, the multi-aperture coherent digital combining system has the technical advantage of the effective mitigation of deep fading under strong turbulence, ease of scalability, and potential higher collected optical power. However, the tricky problem of a multi-aperture system is to efficiently combine multiple branch signals with a static skew mismatch and with time-varying characteristics of received power scintillation. In this Letter, a real-valued massive array multiple-input multiple-output (MIMO) adaptive equalizer is proposed for the first time to our knowledge to realize multi-aperture channel equalization and combining, simultaneously. In the proof-of-principle system, the feasibility of the combining technique is verified based on a MIMO 4 × 2 equalizer in a 2.5-GBaud data rate QPSK modulation FPGA-based two-aperture coherent receiver with a dynamic turbulence simulator. The results show that no reduction in combining efficiency is observed under static turbulence conditions at the hard-decision forward error correction (HD-FEC) limit of 3.8 × 0-3, and combining efficiencies of 95% and 88% are obtained for the dynamic moderate and strong turbulence.

Accurate Electronic and Optical Properties of Organic Doublet Radicals Using Machine Learned Range-Separated Functionals.

Luminescent organic semiconducting doublet-spin radicals are unique and emergent optical materials because their fluorescent quantum yields (Φfl) are not compromised by the spin-flipping intersystem crossing (ISC) into a dark high-spin state. The multiconfigurational nature of these radicals challenges their electronic structure calculations in the framework of single-reference density functional theory (DFT) and introduces room for method improvement. In the present study, we extended our earlier development of ML-ωPBE [J. Phys. Chem. Lett., 2021, 12, 9516-9524], a range-separated hybrid (RSH) exchange-correlation (XC) functional constructed using the stacked ensemble machine learning (SEML) algorithm, from closed-shell organic semiconducting molecules to doublet-spin organic semiconducting radicals. We assessed its performance for a new test set of 64 doublet-spin radicals from five categories while placing all previously compiled 3926 closed-shell molecules in the new training set. Interestingly, ML-ωPBE agrees with the nonempirical OT-ωPBE functional regarding the prediction of the molecule-dependent range-separation parameter (ω), with a small mean absolute error (MAE) of 0.0197 a0-1, but saves the computational cost by 2.46 orders of magnitude. This result demonstrates an outstanding domain adaptation capacity of ML-ωPBE for diverse organic semiconducting species. To further assess the predictive power of ML-ωPBE in experimental observables, we also applied it to evaluate absorption and fluorescence energies (Eabs and Efl) using linear-response time-dependent DFT (TDDFT), and we compared its behavior with nine popular XC functionals. For most radicals, ML-ωPBE reproduces experimental measurements of Eabs and Efl with small MAEs of 0.299 and 0.254 eV, only marginally different from those of OT-ωPBE. Our work illustrates a successful extension of the SEML framework from closed-shell molecules to doublet-spin radicals and will open the venue for calculating optical properties for organic semiconductors using single-reference TDDFT.

Magnesium malate-modified calcium phosphate bone cement promotes the repair of vertebral bone defects in minipigs via regulating CGRP.

Active artificial bone substitutes are crucial in bone repair and reconstruction. Calcium phosphate bone cement (CPC) is known for its biocompatibility, degradability, and ability to fill various shaped bone defects. However, its low osteoinductive capacity limits bone regeneration applications. Effectively integrating osteoinductive magnesium ions with CPC remains a challenge. Herein, we developed magnesium malate-modified CPC (MCPC). Incorporating 5% magnesium malate significantly enhances the compressive strength of CPC to (6.18 ± 0.49) MPa, reduces setting time and improves disintegration resistance. In vitro, MCPC steadily releases magnesium ions, promoting the proliferation of MC3T3-E1 cells without causing significant apoptosis, proving its biocompatibility. Molecularly, magnesium malate prompts macrophages to release prostaglandin E2 (PGE2) and synergistically stimulates dorsal root ganglion (DRG) neurons to synthesize and release calcitonin gene-related peptide (CGRP). The CGRP released by DRG neurons enhances the expression of the key osteogenic transcription factor Runt-related transcription factor-2 (RUNX2) in MC3T3-E1 cells, promoting osteogenesis. In vivo experiments using minipig vertebral bone defect model showed MCPC significantly increases the bone volume fraction, bone density, new bone formation, and proportion of mature bone in the defect area compared to CPC. Additionally, MCPC group exhibited significantly higher levels of osteogenesis and angiogenesis markers compared to CPC group, with no inflammation or necrosis observed in the hearts, livers, or kidneys, indicating its good biocompatibility. In conclusion, MCPC participates in the repair of bone defects in the complex post-fracture microenvironment through interactions among macrophages, DRG neurons, and osteoblasts. This demonstrates its significant potential for clinical application in bone defect repair.

Joint Beamforming and Phase Shifts Design for RIS-Aided Multi-User Full-Duplex Systems in Smart Cities.

Full-duplex (FD) and reconfigurable intelligent surface (RIS) are potential technologies for achieving wireless communication effectively. Therefore, in theory, the RIS-aided FD system is supposed to enhance spectral efficiency significantly for the ubiquitous Internet of Things devices in smart cities. However, this technology additionally induces the loop-interference (LI) of RIS on the residual self-interference (SI) of the FD base station, especially in complicated urban outdoor environments, which will somewhat counterbalance the performance benefit. Inspired by this, we first establish an objective and constraints considering the residual SI and LI in two typical urban outdoor scenarios. Then, we decompose the original problem into two subproblems according to the variable types and jointly design the beamforming matrices and phase shifts vector methods. Specifically, we propose a successive convex approximation algorithm and a soft actor-critic deep reinforcement learning-related scheme to solve the subproblems alternately. To prove the effectiveness of our proposal, we introduce benchmarks of RIS phase shifts design for comparison. The simulation results show that the performance of the low-complexity proposed algorithm is only slightly lower than the exhaustive search method and outperforms the fixed-point iteration scheme. Moreover, the proposal in scenario two is more outstanding, demonstrating the application predominance in urban outdoor environments.

Correction: Gardenia jasminoides J. Ellis extract alleviated white matter damage through promoting the differentiation of oligodendrocyte precursor cells via suppressing neuroinflammation.

Correction for 'Gardenia jasminoides J. Ellis extract alleviated white matter damage through promoting the differentiation of oligodendrocyte precursor cells via suppressing neuroinflammation' by Caixia Zang et al., Food Funct., 2022, 13, 2131-2141, https://doi.org/10.1039/D1FO02127C.

SRS-Net: a universal framework for solving stimulated Raman scattering in nonlinear fiber-optic systems by physics-informed deep learning.

As a crucial nonlinear phenomenon, stimulated Raman scattering (SRS) plays multifaceted roles involved in forward and inverse problems. In fibre-optic systems, these roles range from detrimental interference that impairs optical performance to beneficial effects that enables various devices such as Raman amplifier. To obtain solutions of SRS, various numerical methods customized for different scenarios have been proposed. However, these methods are time-consuming, low-efficiency, and experience-orientated, particularly in combined scenarios consisting of both forward and inverse problems. Inspired by physics-informed neural networks, we propose SRS-Net, which combines the efficient automatic differentiation and powerful representation ability of neural networks with the regularization of SRS physical laws, to obtain universal solutions for SRS of forward, inverse, and combined problems. We showcase the intuitive solving procedure and high-speed performance of SRS-Net through extensive simulations covering different scenarios. Additionally, we validate its capabilities in experiments involving the high-fidelity modelling of a wavelength division multiplexing system spanning the C + L-band with approximately 10 THz. The versatility of the SRS-Net framework extends beyond SRS, indicating its potential as a promising universal solution in other engineering problems with nonlinear dynamics governed by partial differential equations.

The new analogues of ß-trans-bergamotene from endophytic fungus Nigrospora sp. E121 with yam culture medium.

Two new bicyclic sesquiterpenes,Δ9-2, 5, 11-trihydroxyl-ß-cis-bergamotene (3) and Nigrohydroin A (4), together with ten known compounds (1, 2 and 5-12) were obtained from endophytic fungus Nigrospora sp. E121. The structures were elucidated on the basis of their 1D and 2D NMR spectra and mass spectrometric data. The possible biosynthetic pathway of compounds 1, 2, 3 and 4 in Nigrospora sp. E121were reported according to literature. The phytotoxic assay results indicated that the acetyl fragment in α-acetylorcinol may contribute to the phytotoxic activity of this compound.

Decellularized Brain Extracellular Matrix Hydrogel Aids the Formation of Human Spinal-Cord Organoids Recapitulating the Complex Three-Dimensional Organization.

The intricate electrophysiological functions and anatomical structures of spinal cord tissue render the establishment of in vitro models for spinal cord-related diseases highly challenging. Currently, both in vivo and in vitro models for spinal cord-related diseases are still underdeveloped, complicating the exploration and development of effective therapeutic drugs or strategies. Organoids cultured from human induced pluripotent stem cells (hiPSCs) hold promise as suitable in vitro models for spinal cord-related diseases. However, the cultivation of spinal cord organoids predominantly relies on Matrigel, a matrix derived from murine sarcoma tissue. Tissue-specific extracellular matrices are key drivers of complex organ development, thus underscoring the urgent need to research safer and more physiologically relevant organoid culture materials. Herein, we have prepared a rat decellularized brain extracellular matrix hydrogel (DBECMH), which supports the formation of hiPSC-derived spinal cord organoids. Compared with Matrigel, organoids cultured in DBECMH exhibited higher expression levels of markers from multiple compartments of the natural spinal cord, facilitating the development and maturation of spinal cord organoid tissues. Our study suggests that DBECMH holds potential to replace Matrigel as the standard culture medium for human spinal cord organoids, thereby advancing the development of spinal cord organoid culture protocols and their application in in vitro modeling of spinal cord-related diseases.

Human Placenta Decellularized Extracellular Matrix Hydrogel Promotes the Generation of Human Spinal Cord Organoids with Dorsoventral Organization from Human Induced Pluripotent Stem Cells.

Spinal cord organoids are of significant value in the research of spinal cord-related diseases by simulating disease states, thereby facilitating the development of novel therapies. However, the complexity of spinal cord structure and physiological functions, along with the lack of human-derived inducing components, presents challenges in the in vitro construction of human spinal cord organoids. Here, we introduce a novel human decellularized placenta-derived extracellular matrix hydrogel (DPECMH) and, combined with a new induction protocol, successfully construct human spinal cord organoids. The human placenta-sourced decellularized extracellular matrix (dECM), verified through hematoxylin and eosin staining, DNA quantification, and immunofluorescence staining, retained essential ECM components such as elastin, fibronectin, type I collagen, laminin, and so forth. The temperature-sensitive hydrogel made from human placenta dECM demonstrated good biocompatibility and promoted the differentiation of human induced pluripotent stem cell (hiPSCs)-derived spinal cord organoids into neurons. It displayed enhanced expression of laminar markers in comparison to Matrigel and showed higher expression of laminar markers compared to Matrigel, accelerating the maturation process of spinal cord organoids and demonstrating its potential as an organoid culture substrate. DPECMH has the potential to replace Matrigel as the standard additive for human spinal cord organoids, thus advancing the development of spinal cord organoid culture protocols and their application in the in vitro modeling of spinal cord-related diseases.

Photobiomodulation Increases M2-Type Polarization of Macrophages by Inhibiting Versican Production After Spinal Cord Injury.

Spinal cord injury (SCI) is a catastrophic accidence with little effective treatment, and inflammation played an important role in that. Previous studies showed photobiomodulation (PBM) could effectively downregulate the process of inflammation with modification of macrophage polarization after SCI; however, the potential mechanism behind that is still unclear. In the presented study, we aimed to investigate the effect of PBM on the expression level of versican, a matrix molecular believed to be associated with inflammation, and tried to find the mechanism on how that could regulate the inflammation process. Using immunofluorescence technique and western blot, we found the expression level of versican is increased after injury and markedly downregulated by irradiation treatment. Using virus intrathecal injection, we found the knock-down of versican could produce the effect similar to that of PBM and might have an effect on inflammation and macrophage polarization after SCI. To further verify the deduction, we peptide the supernatant of astrocytes to induce M0, M1, and M2 macrophages. We found that the versican produced by astrocytes might have a role on the promotion of M2 macrophages to inflammatory polarization. Finally, we investigated the potential pathway in the regulation of M2 polarization with the induction of versican. This study tried to give an interpretation on the mechanism of inflammation inhibition for PBM in the perspective of matrix regulation. Our results might provide light on the inflammation regulation after SCI.

Determination of orcinol glucoside by LC-MS in Curculigo orchioides and its application to a pharmacokinetic study

ABSTRACT This study was designed to explore the pharmacokinetic regularity of the plasma concentration, tissue distribution and excretion of orcinol glucoside from aqueous extracts of raw and processed Curculigo orchioides Gaertn., Hypoxidaceae. The experiment first used an ultrahigh-performance liquid chromatography-tandem mass spectrometry approach with multiple reaction monitoring and a positive mode to separate orcinol glucoside from naringin to obtain the plasma concentration curves, bar graph of tissue distribution and excretion curves. These results might be beneficial for reasonable clinical application of C. orchioides and for further development of its wine and salt-processing mechanism.