Combining metabolomics and transcriptomics to analyze key response metabolites and molecular mechanisms of Aspergillus fumigatus under cadmium stress.

The co-cultivation of fungi with microalgae facilitates microalgae harvesting and enhances heavy metal adsorption. However, the mechanisms of fungal tolerance to cadmium (Cd) have not yet been studied in detail. In this study, functional groups of fungi were analyzed under Cd stress using Fourier transform infrared spectrometer (FTIR), X-ray photoelectron spectroscopy (XPS), scanning electron microscope (SEM), and transmission electron microscope (TEM) to explore their morphology. Confocal laser scanning microscope (CLSM) was used to characterize the changes in the content of extracellular polysaccharides and proteins, and a decrease in the ratio of glutathione (GSH) to oxidized glutathione (GSSG) was monitored. The GSH and GSSG contents in mycelium were 7.4 and 7.9 times higher than that in the control, respectively. After 72 h of Cd treatment, the fungal extracellular polysaccharide and extracellular protein contents increased by 16 and 11.4 mg/g, respectively, compared to the control. This provided several functional groups for the complexation of Cd ions to enhance fungal Cd tolerance. The metabolomic and transcriptomic results revealed a total of 358 differential metabolites after 20, 48, and 72 h in the positive and negative ion modes, and the number of differential metabolites specific to each group was 104, 14, and 89, respectively. There were 927, 1167, and 1287 up-regulated genes, and 1301, 1480, and 1683 down-regulated genes at 20, 48, and 72 h, respectively. Energy metabolism, amino acid metabolism, and the ABC transport system are the key metabolic pathways for tolerance enhancement and heavy metal detoxification in fungi. The expression of S-cysteinosuccinic acid was significantly up-regulated after Cd stress and associated with enhanced fungal tolerance and resistance to Cd.

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.

Association of exposure to multiple perfluoroalkyl and polyfluoroalkyl substances and glucose metabolism in National Health and Nutrition Examination Survey 2017-2018.

Objective: To investigate the relationships between perfluoroalkyl and polyfluoroalkyl substances (PFASs) exposure and glucose metabolism indices. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) 2017-2018 waves were used. A total of 611 participants with information on serum PFASs (perfluorononanoic acid (PFNA); perfluorooctanoic acid (PFOA); perfluoroundecanoic acid (PFUA); perfluorohexane sulfonic acid (PFHxS); perfluorooctane sulfonates acid (PFOS); perfluorodecanoic acid (PFDeA)), glucose metabolism indices (fasting plasma glucose (FPG), homeostasis model assessment for insulin resistance (HOMA-IR) and insulin) as well as selected covariates were included. We used cluster analysis to categorize the participants into three exposure subgroups and compared glucose metabolism index levels between the subgroups. Least absolute shrinkage and selection operator (LASSO), multiple linear regression analysis and Bayesian kernel machine regression (BKMR) were used to assess the effects of single and mixed PFASs exposures and glucose metabolism. Results: The cluster analysis results revealed overlapping exposure types among people with higher PFASs exposure. As the level of PFAS exposure increased, FPG level showed an upward linear trend (p < 0.001), whereas insulin levels demonstrated a downward linear trend (p = 0.012). LASSO and multiple linear regression analysis showed that PFNA and FPG had a positive relationship (>50 years-old group: ß = 0.059, p < 0.001). PFOA, PFUA, and PFHxS (≤50 years-old group: insulin ß = -0.194, p < 0.001, HOMA-IR ß = -0.132, p = 0.020) showed negative correlation with HOMA-IR/insulin. PFNA (>50 years-old group: insulin ß = 0.191, p = 0.018, HOMA-IR ß = 0.220, p = 0.013) showed positive correlation with HOMA-IR/insulin, which was essentially the same as results that obtained for the univariate exposure-response map in the BKMR model. Association of exposure to PFASs on glucose metabolism indices showed positive interactions between PFOS and PFHxS and negative interactions between PFOA and PFNA/PFOS/PFHxS. Conclusion: Our study provides evidence that positive and negative correlations between PFASs and FPG and HOMA-IR/insulin levels are observed, respectively. Combined effects and interactions between PFASs. Given the higher risk of glucose metabolism associated with elevated levels of PFAS, future studies are needed to explore the potential underlying mechanisms.

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.

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.

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.

Physiological regulation of microalgae under cadmium stress and response mechanisms of time-series analysis using metabolomics.

The investigation of heavy metal wastewater treatment utilizing microalgae adsorption has been extensively demonstrated. However, the response mechanism based on metabolomics to analyze the time-series changes of microalgae under Cd stress has not been described in detail. In this study, SEM/TEM demonstrated that Cd accumulated on the cell surface of microalgae and was bioconcentrated in the cytoplasm, vesicles, and chloroplasts. Carbonyl/quinone/ketone/carboxyl groups (OCO), membrane polysaccharides (OH), and phospholipids (PO) were involved in the interaction of Cd ions, and the chlorophyll content underwent a process of decreasing in the early stage (1.62 mg/g at 48 h) and recovering to the normal level in the late stage, and the contents of MDA, GSH, and SOD were all increased (29.7 nmol/g, 0.23 mg/g, and 30.01 u/106 cells) and then gradually returned to the steady state. The results of EPS content and fluorescent labeling showed that Cd induced the overexpression and synthesis of extracellular polysaccharides and proteins, which is one of the defense mechanisms participating in the reduction of cellular damage by complexed Cd. Metabolomics results indicated that the malate synthesis pathway was activated after Cd-20 h, and the microalgal cells began to shift the metabolic pathway to storage lipid or polysaccharide biosynthesis. In the Calvin cycle, the expression of D-Sedoheptulose 7-phosphate in Cd-20 h_vs_ck and Cd-72 h_vs_Cd-20 h firstly declined and then increased, and the photosynthesis system was suppressed at the beginning, and then gradually returned to normal to maintain the successful development of the dark reaction. The results of time series analysis revealed that the response of microalgae to Cd was categorized into fast response and slow response to regulate cell adsorption and growth metabolism.

Scalable fabrication of free-standing and integrated electrodes with commercial level of areal capacity for aqueous zinc-ion batteries.

Aqueous zinc-ion batteries (AZIBs) present a highly promising avenue for the deployment of grid-scale energy storage systems. However, the electrodes fabricated through conventional methodologies not only suffer from insufficient mass loadings, but also are susceptible to exfoliation under deformations. Herein, a scalable and cost-effective freezing-thawing method is developed to construct free-standing and integrated electrode, comprising H11Al2V6O23.2, carboxymethyl cellulose, and carbon nanotubes. Benefiting from the synergistic effect of these components, the resultant electrode exhibits superior flexibility and robustness, large tensile strength, exceptional electrical conductivity, and favorable electrolyte wettability. Under a large mass loading of 8 mg cm-2 (corresponding to a negative/positive electrode capacity ratio of 2.09), the electrode achieves remarkable capacity of 345.2 mAh/g (2.76 mAh cm-2) at 0.2 A/g and maintains 235.2 mAh/g (1.88 mAh cm-2) at 4 A/g, while sustaining an impressive capacity retention of 97.7 % over 5000 cycles. These considerably outperform conventional electrodes employing traditional binders. Even at an elevated mass loading of 14 mg cm-2 or when operated at a low temperature of - 30 °C, the electrode continues to deliver excellent electrochemical performance (e.g., extraordinary areal capacity of 4.32 mAh cm-2). In addition, the electrode owns outstanding tolerance to external forces. This research contributes to our understanding of the pivotal challenges within the realm of AZIB technology.

Identification of potential immune-related hub genes in Parkinson's disease based on machine learning and development and validation of a diagnostic classification model.

BACKGROUND: Parkinson's disease is the second most common neurodegenerative disease in the world. However, current diagnostic methods are still limited, and available treatments can only mitigate the symptoms of the disease, not reverse it at the root. The immune function has been identified as playing a role in PD, but the exact mechanism is unknown. This study aimed to search for potential immune-related hub genes in Parkinson's disease, find relevant immune infiltration patterns, and develop a categorical diagnostic model. METHODS: We downloaded the GSE8397 dataset from the GEO database, which contains gene expression microarray data for 15 healthy human SN samples and 24 PD patient SN samples. Screening for PD-related DEGs using WGCNA and differential expression analysis. These PD-related DEGs were analyzed for GO and KEGG enrichment. Subsequently, hub genes (dld, dlk1, iars and ttd19) were screened by LASSO and mSVM-RFE machine learning algorithms. We used the ssGSEA algorithm to calculate and evaluate the differences in nigrostriatal immune cell types in the GSE8397 dataset. The association between dld, dlk1, iars and ttc19 and 28 immune cells was investigated. Using the GSEA and GSVA algorithms, we analyzed the biological functions associated with immune-related hub genes. Establishment of a ceRNA regulatory network for immune-related hub genes. Finally, a logistic regression model was used to develop a PD classification diagnostic model, and the accuracy of the model was verified in three independent data sets. The three independent datasets are GES49036 (containing 8 healthy human nigrostriatal tissue samples and 15 PD patient nigrostriatal tissue samples), GSE20292 (containing 18 healthy human nigrostriatal tissue samples and 11 PD patient nigrostriatal tissue samples) and GSE7621 (containing 9 healthy human nigrostriatal tissue samples and 16 PD patient nigrostriatal tissue samples). RESULTS: Ultimately, we screened for four immune-related Parkinson's disease hub genes. Among them, the AUC values of dlk1, dld and ttc19 in GSE8397 and three other independent external datasets were all greater than 0.7, indicating that these three genes have a certain level of accuracy. The iars gene had an AUC value greater than 0.7 in GES8397 and one independent external data while the AUC values in the other two independent external data sets ranged between 0.5 and 0.7. These results suggest that iars also has some research value. We successfully constructed a categorical diagnostic model based on these four immune-related Parkinson's disease hub genes, and the AUC values of the joint diagnostic model were greater than 0.9 in both GSE8397 and three independent external datasets. These results indicate that the categorical diagnostic model has a good ability to distinguish between healthy individuals and Parkinson's disease patients. In addition, ceRNA networks reveal complex regulatory relationships based on immune-related hub genes. CONCLUSION: In this study, four immune-related PD hub genes (dld, dlk1, iars and ttd19) were obtained. A reliable diagnostic model for PD classification was developed. This study provides algorithmic-level support to explore the immune-related mechanisms of PD and the prediction of immune-related drug targets.

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.

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.

Study of molecular patterns associated with ferroptosis in Parkinson's disease and its immune signature.

Parkinson's disease is the second most common neurodegenerative disease in the world. We downloaded data on Parkinson's disease and Ferroptosis-related genes from the GEO and FerrDb databases. We used WCGAN and Random Forest algorithm to screen out five Parkinson's disease ferroptosis-related hub genes. Two genes were identified for the first time as possibly playing a role in Braak staging progression. Unsupervised clustering analysis based on hub genes yielded ferroptosis isoforms, and immune infiltration analysis indicated that these isoforms are associated with immune cells and may represent different immune patterns. FRHGs scores were obtained to quantify the level of ferroptosis modifications in each individual. In addition, differences in interleukin expression were found between the two ferroptosis subtypes. The biological functions involved in the hub gene are analyzed. The ceRNA regulatory network of hub genes was mapped. The disease classification diagnosis model and risk prediction model were also constructed by applying hub genes based on logistic regression. Multiple external datasets validated the hub gene and classification diagnostic model with some accuracy. This study explored hub genes associated with ferroptosis in Parkinson's disease and their molecular patterns and immune signatures to provide new ideas for finding new targets for intervention and predictive biomarkers.

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.

Salting-Out Effect Realizing High-Strength and Dendrite-Inhibiting Cellulose Hydrogel Electrolyte for Durable Aqueous Zinc-Ion Batteries.

Aqueous zinc-ion batteries are limited by poor Zn stripping/plating reversibility. Not only can hydrogel electrolytes address this issue, but also they are suitable for constructing flexible batteries. However, there exists a contradiction between the mechanical strength and the ionic conductivity for hydrogel electrolytes. Herein, high-concentration kosmotropic ions are introduced into the cellulose hydrogel electrolyte to take advantage of the salting-out effect. This can significantly improve both the mechanical strength and ionic conductivity. Additionally, the obtained cellulose hydrogel electrolyte (denoted as Con-CMC) has strong adhesion, a wide electrochemical stability window, and good water retaining ability. The Con-CMC is also found to accelerate the desolvation process, improve Zn deposition kinetics, promote Zn deposition along the (002) plane, and suppress parasitic reactions. Accordingly, the Zn/Zn cell with Con-CMC demonstrates dendrite-free behavior with prolonged lifespan and can endure extremely large areal capacity of 25 mAh cm-2. The Con-CMC also enables a large average Coulombic efficiency of 99.54% over 500 cycles for the Zn/Cu cell. Furthermore, the assembled pouch-type Zn/polyaniline full battery provides great rate capability, superior cyclability (even with limited Zn anode excess), a slow self-discharge rate, and outstanding affordability to external forces. Overall, this work extends our knowledge of the rational design of hydrogel electrolytes.

Multiple Dense Papules on the Entire Glans: Profound Pearly Penile Papules.

A 23-year-old man presented for evaluation of multiple dense asymptomatic papules on the entire glans. Histologically, the lesions resembled acral angiofibroma. A diagnosis of profound pearly penile papules was made. This is the third reported case and more serious and typical than described in previous reports.

High-performance Si@C anode for lithium-ion batteries enabled by a novel structuring strategy.

Si anode has drawn growing attention because of its features of large specific capacity, low electrochemical potential, and high natural abundance. However, it suffers from severe electrochemical irreversibility due to its large volume change during cycling. In spite of the achievement of improved electrochemical performance after compositing with carbon materials, most of the reported Si/C composite anodes lack a simple preparation process. To obtain a promising Si-based anode material, both simple preparation process and improved performance are necessary. Herein, inspired by the structure of shock proof foam, a novel structure of Si-based composite (Si@FeNO@P), consisting of Si nanoparticles embedded within a highly graphitized Fe3C/Fe3O4 hybrid nanoparticle-interspersed foam-like porous carbon matrix, has been constructed using a simple method, consisting of simple mixing, drying, and carbonization processes. Thus, the well-designed composite structure effectively mitigates issues resulting from volumetric change of the Si during cycle and hence improves its performance significantly. The research results confirm outstanding performance of the Si@FeNO@P anode in the aspects of cycle durability, specific capacity, and rate capability, with 1116.1 (250th cycle), 858.1 (500th cycle), and 503.1 (500th cycle) mA h g-1 at 100, 1000, and 5000 mA g-1, respectively. By comparing the performance and structure of Si@FeNO@P with other control samples, it was substantiated that the outstanding performances of the Si@FeNO@P anode depend on the synergistic effects of the well-designed unique carbon matrix, conductive Fe3C, and Fe3O4-in situ derived metallic Fe nanoparticles during cycling. The outstanding electrochemical performance and simple preparation route make the Si@FeNO@P anode promising for lithium-ion battery applications. This work also gives useful insights into the development of high-performance Si-based anodes with simple practical methods.

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.

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.