Moderate-intensity continuous training and high-intensity interval training alleviate glycolipid metabolism through modulation of gut microbiota and their metabolite SCFAs in diabetic rats.

Glucose and lipid metabolism disorders are typical of diabetic patients and are important factors leading to macrovascular and microvascular complications. The aim of this study was to understand the effects of different exercises on glycolipid metabolism in diabetic rats and the role of gut flora in metabolic maintenance. We measured glycolipid metabolic indices and short-chain fatty acids (SCFAs) content and sequenced and analyzed gut microbes after 8 weeks of moderate-intensity continuous training (MICT) and high-intensity interval training (HIIT) programs in type 2 diabetic rats(T2DM). We found that Enterococcaceae, Enterococcus, Subdoligranulum, Kurthia, Bacillales, and Planococcaceae may be key bacterial taxa related to T2DM and that both programs of exercise regulated the intestinal flora of rats with T2DM, improved their glycolipid metabolism, increased the abundance of SCFA-producing intestinal bacteria, and it was found that the PWY-5676 and P163-PWY pathways which are closely related to production of SCFAs were significantly upregulated in the exercise groups. Notably, MICT appeared to be more effective than HIIT in increasing the homogeneity of rat intestinal flora, enriching species, and increasing acetic acid and butyric acid content. These results suggest that exercise improves glycolipid metabolism in diabetic rats, which may be attributed to alterations in the structure of their intestinal flora.

End-to-end learning strategy based on a frequency domain feature decoupling network emulator with joint probabilistic shaping and equalization for a 300-Gbit/s OAM mode division multiplexing transmission.

Mode coupling and device nonlinear impairment appear to be a long-standing challenge in the orbital angular momentum (OAM) mode division multiplexing (MDM) of intensity modulation direct detection (IM/DD) transmission systems. In this paper, we propose an end-to-end (E2E) learning strategy based on a frequency domain feature decoupling network (FDFDnet) emulator with joint probabilistic shaping (PS) and equalization for an OAM-MDM IM/DD transmission with three modes. Our FDFDnet emulator can accurately build a complex nonlinear model of an OAM-MDM system by separating the signal into features from different frequency domains. Furthermore, a FDFDnet-based E2E strategy for joint PS and equalization is presented with the aim of compensating the signal impairment for the OAM-MDM IM/DD system. An experiment is carried out on a 300 Gbit/s carrierless amplitude phase-32 (CAP-32) signal with three OAM modes over a 10 km ring-core fiber transmission, and the results show that the proposed FDFDnet emulator outperforms the traditional CGAN emulator, with improvements in the modelling accuracy of 30.8%, 26.3% and 31% for the three OAM modes. Moreover, the receiver sensitivity of the proposed E2E learning strategy is higher than for the CGAN emulator by 3, 2.5, 2.2 dBm and the real channel by 5.5, 5.1, and 5.3 dBm for the three OAM modes, respectively. Our experimental results demonstrate that the proposed FDFDnet emulator-based E2E learning strategy is a promising contender for achieving ultra-high-capacity interconnectivity between data centers.

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. Therefore, although the transmission symbols in the WDM-OAM-MDM system are subjected to a stochastic nonlinear impairment, the proposed LANFIS equalizer can effectively compensate the distorted signals. The proposed equalizer outperforms the Volterra equalizer with improvements in receiver sensitivity of 2, 1.5, and 1.3 dB for three OAM modes at a wavelength of 1550.12 nm, respectively. It also outperforms a CNN equalizer, with improvements in receiver sensitivity of 1, 0.5, and 0.3 dB, respectively. Moreover, complexity reductions of 67%, 74%, and 99.9% are achieved for the LANFIS equalizer compared with the Volterra, CNN, and ANFIS equalizers, respectively. The proposed equalizer has high performance and low complexity, making it a promising candidate for a high-speed WDM-OAM-MDM system.

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. Experimental results show nonlinear equalization of 120 Gb/s PS 64QAM signals in a 375 km transmission scenario. The proposed DACNN equalizer improves the receiver sensitivity by 2.6 dB and 1.1 dB over the Volterra equalizer and convolutional neural network (CNN) equalizer, respectively. Meanwhile, DACNN converges with fewer training epochs than CNN, which provides great potential for mitigating the nonlinear distortion of PS signals in fiber optic communication systems.

16QAM optical signal generation scheme based on weighted optimal Euclidean distance.

A two-dimensional signal constellation scheme for binary uniform memoryless source transmission in optical fiber channels is studied in this paper. In geometric shaping (GS), optimization algorithms are usually used to change the overall position of constellation points while maintaining the probability of constellation points unchanged. Different optimization functions are used to allocate the position of constellation symbols, thereby improving constellation performance. A 16 quadrature amplitude modulation (QAM) optical signal generation scheme based on weighted optimal Euclidean distance is proposed in this paper. In order to obtain the best constellation diagram and increase the shaping gain, the weighted optimal Euclidean distance that can minimize the bit error rate (BER) over multiple iterative optimizations is used as the objective function. On the one hand, the proposed 16QAM optical signal generation scheme based on weighted optimal Euclidean distance always outperforms the uniform square 16QAM and the uniform circle 16QAM schemes in the back to back (BTB) transmission. On the other hand, after analyzing the simulation demonstration in a 50GBaud coherent optical communication system over 3000 km, results demonstrate that the optical signal to noise ratio (OSNR) performance of this system is better than that of the uniform square 16QAM and the uniform circle 16QAM, which is improved by 0.52 dB and 0.85 dB, respectively. In addition, the proposed 16QAM system increases the transmission distance by 989 km and 741 km, respectively, compared to the other two systems. The performance confirms that the proposed novel 16QAM scheme, to the best of our knowledge, can effectively improve the reliability and transmission distance. Therefore, the proposed scheme has a certain development prospect in the future long-distance transmission of high-speed optical fiber communication.

Poly (lactic-co-glycolic acid)-encapsulated Endostar-loaded calcium phosphate cement as anti-tumor bone cement for the treatment of bone metastasis in lung cancer.

Lung cancer is one of the most common malignant tumors in the world. In approximately 30%-40% of lung cancer patients, bone metastases ensues with osteolytic destruction. Worse still, intractable pain, pathological fracture, and nerve compression caused by bone metastases are currently the bottleneck of research, diagnosis, and treatment of lung cancer. Therefore, the present study aims at investigating the effectiveness of a new composite material made of calcium phosphate cement (CPC) and Endostar on repairing bone defects in vitro and in vivo. As indicated in results, the mechanical properties of CPC+Endostar and CPC+PLGA+Endostar do not differ from those of pure CPC. The PLGA-embedded Endostar slow-release microspheres were designed and prepared, and were combined with CPC. Poly (lactic-co-glycolic acid (PLGA) is a biodegradable polymer material in vivo, so the effect on its mechanical properties is negligible. CPC+Endostar and CPC+PLGA+Endostar have been proved to inhibit cell proliferation, promote apoptosis and block cell cycle in G2 phase; the expression levels of osteoclast-related genes CXCL2, TGF-ß1, IGF-1, IL-6, and RANKL were significantly decreased while osteogenic ability and alkaline phosphatase activity observably enhanced. In vivo studies have revealed that the expression levels of TRAP, RANKL, and Caspase3 in CPC+PLGA+ENDO-treated tumor tissues after 3 weeks were higher than those in other groups with the prolongation of animal treatment time, while the expression levels of OPN and BCL2 were lower than those in other groups. In hematoxylin and eosin and TUNEL staining, 3 weeks of CPC+PLGA+ENDO-treatment yielded higher tissue necrosis and apoptosis than other groups; computed tomography and magnetic resonance imaging results showed the posterior edge bone damage reduced as a result of the CPC+PLGA+ENDO grafting in vertebral pedicle. Overall, the feasibility and reliability of CPC-loaded Endostar in the treatment of bone metastasis in lung cancer were investigated in this study, so as to promote the basic research and treatment of bone metastasis in lung cancer and other malignant tumors.

Data rate measurement method with selectable range in linear optical sampling.

Linear optical sampling (LOS) is one of the most powerful techniques for high-speed signal monitoring. To measure the data-rate of signal under test (SUT) in optical sampling, multi-frequency sampling (MFS) was proposed. However, the measurable data-rate range of the existing method based on MFS is limited, which makes it very difficult to measure the data-rate of high-speed signals. To solve the above problem, a range selectable data-rate measurement method based on MFS in LOS is proposed in this paper. Through this method, the measurable data-rate range can be selected to match the data-rate range of SUT and the data-rate of SUT can be measured precisely, independently of the modulation format. What's more, the sampling order can be judged using the discriminant in the proposed method, which is key for plotting eye diagrams with correct time information. We experimentally measure the baud-rates of PDM-QPSK signal from 800 MBaud to 40.8 GBaud in different ranges and judge the sampling orders. The relative error of measured baud-rate is less than 0.17% while the error vector magnitude (EVM) is less than 0.38. Compared with the existing method, under the same sampling cost, our proposed method realizes the selectivity of the measurable data-rate range and the judgment of sampling order, greatly extends the measurable data-rate range of SUT. Hence, the data-rate measurement method with selectable range has great potential for high-speed signal data-rate monitoring.

Bayesian generative adversarial network emulator based end-to-end learning strategy of the probabilistic shaping for OAM mode division multiplexing IM/DD transmission.

Orbital angular momentum (OAM) mode division multiplexing (MDM) has emerged as a new multiplexing technology that can significantly increase transmission capacity. In addition, probabilistic shaping (PS) is a well-established technique that can increase the transmission capacity of an optical fiber to close to the Shannon limit. However, both the mode coupling and the nonlinear impairment lead to a considerable gap between the OAM-MDM channel and the conventional additive white Gaussian noise (AWGN) channel, meaning that existing PS technology is not suitable for an OAM-MDM intensity-modulation direct-detection (IM-DD) system. In this paper, we propose a Bayesian generative adversarial network (BGAN) emulator based on an end-to-end (E2E) learning strategy with probabilistic shaping (PS) for an OAM-MDM IM/DD transmission with two modes. The weights and biases of the BGAN emulator are treated as a probability distribution, which can be accurately matched to the stochastic nonlinear model of OAM-MDM. Furthermore, a BGAN emulator based on an E2E learning strategy is proposed to find the optimal probability distribution of PS for an OAM-MDM IM/DD system. An experiment was conducted on a 200 Gbit/s two OAM modes carrierless amplitude phase-32(CAP-32) signal over a 5 km ring-core fiber transmission, and the results showed that the proposed BGAN emulator outperformed a conventional CGAN emulator, with improvements in modelling accuracy of 29.3% and 26.3% for the two OAM modes, respectively. Moreover, the generalized mutual information (GMI) of the proposed E2E learning strategy outperformed the conventional MB distribution and the CGAN emulator by 0.31 and 0.33 bits/symbol and 0.16 and 0.2 bits/symbol for the two OAM modes, respectively. Our experimental results demonstrate that the proposed E2E learning strategy with the BGAN emulator is a promising candidate for OAM-MDM IM/DD optic fiber communication.

Secure key real-time update and dynamic DNA encryption for CO-OFDM-PON based on a hybrid 5-D chaos.

A double key (DK) real-time update and hybrid five-dimensional (5-D) hyperchaotic deoxyribonucleic acid (DNA) dynamic encryption scheme is proposed, which can ensure the security in the orthogonal frequency division multiplexing passive optical network (OFDM-PON). Chaotic sequences for DNA dynamic encryption are produced using a four-dimensional (4-D) hyperchaotic Lü system and a one-dimensional (1-D) logistic map. In this scheme, the DK consists of an external key set, which is stored locally, and an internal key, which is associated with the plaintext and external key. In addition, a pilot cluster is used as the carrier of key transmission and key embedding is achieved by converting key to phase information of the pilot. To verify the feasibility of the scheme, a simulation validation is performed on a 46.5Gb/s 16 quadrature amplitude modulation (QAM) coherent OFDM-PON system transmitted over an 80 km transmission distance. The results show that the proposed scheme can improve the security performance of OFDM-PON at a low OSNR cost of 0.3 dB and the key space is expanded to (8.514 × 10102)S. When the correlation redundancy (CR) G⩾7, the 0 bit error rate (BER) of key can be achieved and the key can be updated and distributed in real-time without occupying additional secure channels.

400 Gbit/s 4 mode transmission for IM/DD OAM mode division multiplexing optical fiber communication with a few-shot learning-based AffinityNet nonlinear equalizer.

Nonlinear impairment in a high-speed orbital angular momentum (OAM) mode-division multiplexing (MDM) optical fiber communication system presents high complexity and strong stochasticity due to the massive optoelectronic devices. In this paper, we propose an Affinity Network (AffinityNet) nonlinear equalizer for an OAM-MDM intensity-modulation direct-detection (IM/DD) transmission with four OAM modes. The labeled training and testing signals from the OAM-MDM system can be regarded as "small sample" and "large target", respectively. AffinityNet can be used to build an accurate nonlinear model using "small sample" based on few-shot learning and can predict the stochastic characteristic nonlinearity of OAM-MDM with a high level of generalization. As a result, the AffinityNet nonlinear equalizer can effectively compensate the stochastic nonlinearity in the OAM-MDM system, despite the large difference between the training and testing signals due to the stochastic nonlinear impairment. An experiment was conducted on a 400 Gbit/s transmission with four OAM modes using a pulse amplitude modulation-8 (PAM-8) signal over a 2 km ring-core fiber (RCF). Our experimental results show that the proposed nonlinear equalizer outperformed the conventional Volterra equalizer with improvements in receiver sensitivity of 1.7, 1.8, 3, and 3.3 dB for the four OAM modes at the 15% forward error correction (FEC) threshold, respectively. In addition, the proposed equalizer outperformed a convolutional neural network (CNN) equalizer with improvements in receiver sensitivity of 0.8, 0.5, 0.9, and 1.4 dB for the four OAM modes at the 15% FEC threshold. In the experiment, a complexity reduction of 37% and 83% of the AffinityNet equalizer is taken compared to the conventional Volterra equalizer and CNN equalizer, respectively. The proposed equalizer is a promising candidate for a high-speed OAM-MDM optical fiber communication system.

Hidden conditional random field-based equalizer for the 3D-CAP-64 transmission of OAM mode-division multiplexed ring-core fiber communication.

As a key technique for achieving ultra-high capacity optical fiber communications, orbital angular momentum (OAM) mode-division multiplexing (MDM) is affected by severe nonlinear impairments, including modulation related nonlinearities, square-law nonlinearity and mode-coupling-induced nonlinearity. In this paper, an equalizer based on a hidden conditional random field (HCRF) is proposed for the nonlinear mitigation of OAM-MDM optical fiber communication systems with 20 GBaud three-dimensional carrierless amplitude and phase modulation-64 (3D-CAP-64) signals. The HCRF equalizer extracts the stochastic nonlinear feature of the OAM-MDM 3D-CAP-64 signals by estimating the conditional probabilities of the hidden variables, thereby enabling the signals to be classified into subclasses of constellation points. The nonlinear impairment can then be mitigated based on the statistical probability distribution of the hidden variables of the OAM-MDM transmission channel in the HCRF equalizer. Our experimental results show that compared with a convolutional neural network (CNN)-based equalizer, the proposed HCRF equalizer improves the receiver sensitivity by 2 dB and 1 dB for the two OAM modes used here, with l = + 2 and l = + 3, respectively, at the 7% forward error correction (FEC) threshold. When compared with a Volterra nonlinear equalizer (VNE) and CNN-based equalizer, the computational complexity of the proposed HCRF equalizer was found to be reduced by 30% and 41%, respectively. The bit error ratio (BER) performance and reduction in computational complexity indicate that the proposed HCRF equalizer has great potential to mitigate nonlinear distortions in high-speed OAM-MDM fiber communication systems.

Low-complexity characterized-long-short-term-memory-aided channel modeling for optical fiber communications.

In this work, a low-complexity data-driven characterized-long-short-term-memory (C-LSTM)-aided channel modeling technique is proposed for optical single-mode fiber (SMF) communications. To fully utilize the sequence correlation learning ability of traditional long short-term memory (LSTM) networks and solve the gradient explosion problem, the feature information is introduced into the traditional LSTM input layer to better characterize the intersymbol interference caused by dispersion in SMF modeling. The simulation results show that the proposed C-LSTM can effectively alleviate the gradient explosion problem with a stable and ultimately lower mean square error (MSE) than traditional LSTM. Compared with the split-step Fourier method (SSFM) and the conditional generative adversarial network (CGAN), the proposed C-LSTM has superior computational complexity. Moreover, due to the sequence correlation learning ability inherent to C-LSTM, coupled with the flexibility of feature information selection, the proposed C-LSTM-aided modeling technique has a higher modeling accuracy than traditional LSTM. Moreover, the C-LSTM-aided modeling technique can be effectively extended to other channel modeling applications with strong sequence correlations.

Aerobic exercise improves intestinal mucosal barrier dysfunction through TLR4/MyD88/NF-κB signaling pathway in diabetic rats.

This study examined the effects of aerobic exercise on the intestinal mucosal barrier dysfunction in diabetic rats. We established a diabetic rats model consisting of three groups: normal control (NC), diabetes control (DC), and diabetes eight-week aerobic exercise (DE). We measured serum fasting blood glucose (FBG), insulin (INS), diamine oxidase (DAO), D(-)-lactate (D-Lac), lipopolysaccharide (LPS), tumour necrosis factor-α (TNF-α), interleukin-6 (IL-6), and insulin resistance index (HOMA-IR). Intestinal sections of tissue were stained with H&E and examined using transmission electron microscopy. Expressions of occludin, claudin-1, toll-like receptor-4 (TLR4), myeloid differentiation primary response protein 88 (MyD88), and nuclear factor-κB (NF-κB) in small intestinal mucosa were determined by Western Blot. In comparison to NC, FBG, HOMA-IR, DAO, D-Lac, TNF-α, IL-6, and LPS were increased (P < 0.05) in DC, whereas INS, villus height, crypt depth, and mucosal thickness were decreased (P < 0.05). In comparison to DC, FBG, DAO, D-Lac, TNF-α, and LPS were decreased (P < 0.05) in DE, whereas INS, villus height, crypt depth, and mucosal thickness were increased (P < 0.05). In comparison to NC, occludin and claudin-1 were decreased (P < 0.05) in DC, whereas TLR4, MyD88, and NF-κB were increased (P < 0.05). In comparison to DC, occludin and claudin-1 were increased (P < 0.05) in DE, whereas TLR4, MyD88, and NF-κB were decreased (P < 0.05). In conclusion, eight-week aerobic exercise improved intestinal mucosal barrier dysfunction in diabetic rats, by inhibiting LPS release, TLR4/MyD88/NF-κB signaling pathway, and pro-inflammatory cytokines expression.

Fast linear optical sampling with high repetition-frequency using fiber delay lines.

Linear optical sampling (LOS) is one of the most promising techniques for optical modulation analyzers. The LOS system generally adopts a mode-locked fiber laser (MFL) to generate an ultra-stable optical pulse to realize under-sampling for signal under test (SUT). However, it is challenging for MFL to produce a high-repetition-frequency pulse, making more measurement errors of conventional LOS technology, especially for high-speed signals. This paper proposes a dual-pulse mixing (DPM) based LOS system to increase the repetition frequency using fiber delay lines with the multiplied optical pulse. We propose the pulse location and peak extraction algorithms to compensate the time bias and amplitude bias in the DPM-based LOS system, which significantly improves the measurement speed and range. The experiment results show that the DPM-based LOS system can increase the number of sampling points twice compared with the conventional LOS within the same sampling time window. Furthermore, the proposed DPM-based LOS system can achieve less error vector magnitude with a reduction of 9.1% compared with the conventional LOS. Hence, the proposed DPM-based LOS system has great potential for high-speed signal processing.

Security enhancement for adaptive optics aided longitudinal orbital angular momentum multiplexed underwater wireless communications.

The frozen-wave-based longitudinal orbital angular momentum multiplexing (LOAMM) system developed in [IEEE Photonics J.10, 7900416 (2018)10.1109/JPHOT.2017.2778238] has the potential to overcome the crosstalk effects induced by turbulence. In this paper, we propose a defocus measurement aided adaptive optics (DMA-AO) technique for turbulence compensation in a LOAMM underwater wireless optical communication (UWOC) system to investigate the enhancement of physical layer security. Relying on a phase retrieval algorithm and probe beam, three amplitude-only measurements obtained from different back focus planes can realize phase reconstruction of distorted OAM beams. Moreover, the so-called mixture generalized gamma-Johnson SB (GJSB) distribution is proposed to characterize the probability density function (PDF) of reference-channel irradiance of OAM. The GJSB allows for obtaining closed-form and analytically tractable expression for the probability of strictly positive secrecy capacity (SPSC) in a single input single output (SISO) system. Furthermore, the average secrecy capacity (ASC) and probability of SPSC for a multiple input multiple output (MIMO) system are investigated. Compared to the traditional OAM multiplexing system based on Laguerre-Gaussian (LG) beams, the LOAMM system with a probe beam assisted DMA-AO technique has potential advantages for improving the security performance in UWOC.

Chaotic physical layer encryption scheme based on phase ambiguity for a DMT system.

Chaotic encryption is a promising scheme for physical layer security. By solving the multi-dimensional chaotic equations and transforming the obtained results, both bit-level and symbol-level encryption can be realized. One of the mainstream symbol-level encryption solutions is the constellation shifting (CS) scheme, which treats the chaotic sequence as artificial noise and adds it to the QAM signal sequence to achieve encryption. However, this scheme has several technical flaws in practical application, in terms of computational complexity and coexistence with blind equalization algorithm and the probabilistic shaping (PS) technique. In this paper, we propose a novel symbol-level encryption scheme based on phase ambiguity (PA), which converts the two sequences originally used to generate artificial noise into a set of phase rotation keys and complex conjugate keys, so that the encrypted symbols are still on the ideal constellation point coordinates. Simulation verification is carried out in a discrete multi-tone (DMT) system with 64QAM modulation. Results show that the proposed scheme can fully retain the shaping gain brought by the PS technique and avoid the error convergence of the blind equalizer. Moreover, the time required to solve the chaotic equations is only 38% of the CS scheme. Experimental verification is carried out, and the obtained results once again prove the superiority of the proposed encryption algorithm, which is a practical alternative for future physical layer secure optical communications.

Probabilistic neural network equalizer for nonlinear mitigation in OAM mode division multiplexed optical fiber communication.

Orbital angular momentum (OAM) mode-division multiplexing (MDM) is a key technique to achieve ultra-high-capacity optical fiber communications. However, the high nonlinear impairment from optoelectronic devices, such as spatial light modulators, modulators, and photodiodes, is a long-standing challenge for OAM-MDM. In this paper, an equalizer based on a probabilistic neural network (PNN) is presented to mitigate the nonlinear impairment for an OAM-MDM fiber communication system with 32 GBaud Nyquist pulse amplitude modulation-8 (PAM8) intensity-modulation direct-detection (IM-DD) signals. PNN equalizer can calculate the distribution of the nonlinearity using Bayesian decision theory and thus mitigate the stochastic nonlinear impairment of the received signal. Experimental results show that compared with the convolutional neural network (CNN) equalizer, the PNN equalizer improves the receiver sensitivity by 0.6dB and 2dB for two OAM modes with l = + 3 and l = + 4 at the 20% FEC limit, respectively. Moreover, compared with Volterra or CNN equalizers, the PNN equalizer can reduce the computation complexity significantly, which has great potential to mitigate the nonlinear signal distortions in high-speed IM-DD OAM-MDM fiber communication systems.

Mitigating the ambiguity problem in the CNN-based wavefront correction.

In this work, we propose an attention-based adaptive optics method that uses a non-local block to integrate phase diversity with a convolutional neural network (CNN). The simulation results showcase the effectiveness of the proposed method to mitigate the ambiguity problem of phase retrieval and better performance than traditional CNN-based wavefront correction.

Dynamic routing, modulation, and spectrum assignment based on fuzzy logic control in elastic optical networks.

Considering the constantly changing network resources and randomly generated spectrum fragmentation problem, a dynamic routing, modulation, and spectrum assignment based on the fuzzy logic control (RMSA-FLC) algorithm is proposed in this paper. A fuzzy logic control (FLC) system based on the degree of fragmentation optimization and the degree of link selection is constructed. The path-control weight (PW) is achieved as the output of the FLC system, and the path and spectrum allocation scheme with the maximum PW is selected for the immediate reservation requests. The simulation results show that the proposed RMSA-FLC algorithm can effectively reduce the blocking probability and improve the spectrum resource utilization in a dynamic elastic optical network.

Effect of extracellular proteins on Cd(II) adsorption in fungus and algae symbiotic system.

Fungus-algae symbiotic systems (FASS) are typically used to assist in the immobilization of algae and strengthen the adsorption of heavy metals. However, the adsorption behavior of the symbiotic system and the molecular regulation mechanism of extracellular proteins in the adsorption of heavy metals have not been reported in detail. In this study, a stable FCSS (fungus-cyanobacterium symbiotic system) was used to study Cd(II) adsorption behavior. The fixation efficiency of fungus to cyanobacterium reached more than 95% at pH7.0, 30 °C, 150 rpm, and a medium ratio of 100%. The biomass, chlorophyll content, and total fatty acid content of the symbiotic system were much higher than those of cyanobacterium and fungus alone. The photosynthetic fluorescence parameters showed that the presence of fungus enhanced the light tolerance of cyanobacterium. The original light energy conversion efficiency and potential activity of PSII were enhanced, indicating that symbiosis could promote the photosynthetic process of cyanobacterium. The Cd(II) adsorption efficiency can achieve 90%. The system maintained excellent adsorption after six adsorption cycles. Differential proteins were mainly enriched in areas such as metabolism, ABC transport system, and pressure response. Cd(II) stress promotes an increase in efflux proteins. Moreover, cadmium can be fixed as much as possible by secreting extracellular proteins, and the toxicity of cadmium to cells can be alleviated by regulating the metabolism of glutathione, reducing oxidative phosphorylation level, and reducing oxidative stress, thus improving the resistance to Cd(II). Meanwhile, the expression of enzymes involved in glycolysis and the pentose phosphate pathway was upregulated, while the expression of those in the TCA cycle was downregulated. The expression of substances related to PSI and PSII in the photosynthetic system and rubisco, a key enzyme in the Calvin cycle, was significantly upregulated, indicating that the glucose metabolism and photosynthetic pathways of the symbiotic system were involved in resistance to Cd toxicity. This revealed the response mechanism of the fungus-algal symbiotic system in the process of Cd adsorption, and also provided reference value for industrial application in water treatment.